AN INTELLIGENT SYSTEM FOR TYPE II DIABETES ...

AN INTELLIGENT SYSTEM FOR TYPE II DIABETES

MELLITUS DIAGNOSTIC

A Thesis

Submitted to the Faculty of Graduate Studies and Research

In Partial Fulfillment of the Requirements

For the Degree of

Master of Applied Science

Industrial Systems Engineering

University of Regina

Gizem Koca

Regina, Saskatchewan

June, 2020

UNIVERSITY OF REGINA

FACULTY OF GRADUATE STUDIES AND RESEARCH

SUPERVISORY AND EXAMINING COMMITTEE

Gizem Koca, candidate for the degree of Master of Applied Science in Industrial Systems Engineering, has presented a thesis titled, An Intelligent System for Type II Diabetes Mellitus Diagnostic, in an oral examination held on May 4, 2020. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material. External Examiner: *Dr. Lei Zhang, Electronic Systems Engineering

Supervisor: *Dr. Rene Mayorga, Industrial Systems Engineering

Committee Member: *Dr. Wei Peng, Faculty of Engineering & Applied Science

Committee Member: *Dr. Adisorn Aroonwilas, Industrial Systems Engineering

Chair of Defense: *Dr. Ernest Johnson, Faculty of Business Administration *via ZOOM Conferencing

ABSTRACT

Diabetes mellitus is a modern world burden mentally, physically, and economically. Diabetes

mellitus occurs when the blood sugar level reaches too high levels. Some of the significant

complications of diabetes mellitus are cardiovascular diseases, major organ damages,

Alzheimer's, depression, and in further stage losing a life.

The diagnosis of diabetes mellitus is a complicated and time-consuming problem. In the

diagnosis process, a medical expert has to investigate many factors, such as age, gender, body

mass index and blood glucose level. Also, after the first laboratory tests and medical exam

results, if the first laboratory test is affirmative for disease, the confirmatory test result should

check for the diagnostic decision.

Although some intelligent systems have been used for diagnosing diabetes mellitus in the

previous studies, the diagnosis problem of diabetes mellitus could not be solved due to the lack

of possible variables usage, the variance of patients, uncertainty, and vagueness. This Thesis is

based on Fuzzy Logic, which can efficiently deal with human logic. Also, Fuzzy Logic is a

vigorous and productive development for solving medical problems efficiently and effectively.

Fuzzy Logic creates chances for easy checking of the system, adding or deleting more inputs

and changing the fuzzy conditional statements for further developments. This Thesis seeks to

develop an intelligent system based on MATLAB that can help medical practitioners in the

diagnosis of type 2 diabetes mellitus.

The proposed system consists of five different Fuzzy Inference Systems, two central and three

subsystems. The main systems diagnose the patient using the subsystems. The Primary

diagnostic system uses personal features, biological features, and lifestyle habits; while the

Secondary diagnostic system considers personal features and morphological features. The

reason behind using two different central diagnostic systems is that in some cases, the user of

the proposed system cannot know the patient lifestyle habits. In these kinds of situations, the

user can ignore the patient lifestyle habit quickly by using the second central system. The input

variables are age, gender, nationality, body mass index, family history, systolic blood pressure,

diastolic blood pressure, cholesterol level, blood glucose level, pregnancy situation, dietary

factors, physical activity level, smoking habit, and alcohol consumption.

Furthermore, the proposed system performance has been evaluated with accuracy, sensitivity,

and specificity using three different diabetes datasets: Pima Indian Diabetes Dataset (PIDD),

BioStatistics Diabetes Dataset (BSDD) and Randomized produced dataset (RPD). The number

of attributes is different for each dataset. There are six attributes for PIDD, eight attributes for

BSDD and 14 attributes for RPD. The specificity of the proposed system is 69.2% for PIDD,

92.5% for BSDD, and 95.89% for RPD; while the sensitivity of the proposed method is 93.75%

for PIDD, 98.33% for BSDD, and 100% for RPD. Also, the performance evaluation results

show that when the number of attributes is lower than system needs, the performance of the

proposed system reduces drastically.

Consequently, this Thesis determined that an intelligent system for diagnosis for type 2 diabetes

mellitus and the proposed system will prove that Fuzzy Logic applications for the diagnostic

problem will be a successful paradigm.

Keywords: Fuzzy Logic, Fuzzy Set Theory, Fuzzy Inference System, Mamdani Fuzzy Inference

System, Type 2 Diabetes Mellitus, Prediabetes, Diabetes Mellitus

ACKNOWLEDGEMENTS

I would like to express my special gratitude to my supervisor, Professor Rene V. Mayorga,

who suggested this Thesis topic to me, and provided me with valuable advice, continuous

support, and guidance throughout my studies. He was always a light in the darkness for my

dilemmas and questions. Without his passionate support, participation, and guidance, I would

not have been able to overcome my concerns and reach success and sincerity.

I would like to thank the Faculty of Graduate Studies and Research and Faculty of Engineering

and Applied Science at the University of Regina for providing me with a teaching assistantship

(09/2018-12/2018 and 09/2019-12/2019) and graduate student travel award (09/2019). Also, I

would like to express my appreciation to the Saskatchewan Center for Patient-Oriented

Research (SCPOR) for supporting my Thesis work financially.

Furthermore, I would also like to appreciate my all beloved family members, especially my

parents (Sadiye and Celal), my sister (Cigdem) and my aunt (Yasemin), for their unlimited

support through my life with their love, compassion, guidance, support and encouragement that

I needed to complete my Thesis.

Finally, I would like to thank the people that have been supporting me always, my friends Baran

Ozdemir, Dilara Omur, Divya Shinde, Duygu Kural, Ipek Dilsiz, Maricarmen Tay Lee Sanchez,

Mohammad Tuohidul Alam Bhuiyan, Nazli Saylam, Rabia Gizem Demirci Sarikurt, Turgan

Yalcin, Shahnaz Habibkhah, Sevgi Gunindi Dogan, and family friends Hilal Kardes Dag, Leyla

Okatan, Seyhan Sahiner, Turkan Seda Tan and Tuncer Tuna. Ultimately, all the people who

have helped me with their ideas, innovations, and advice, especially all the professors and

classmates.

POST DEFENCE ACKNOWLEDGEMENTS

I would like to show my deepest appreciation to my committee members, Dr. Lei Zhang, Dr.

Adisorn Aroonwilas and Dr. Wei Peng, and the chair of my defence, Dr. Ernest Johnson, for

their evaluation of my Thesis and their presence in my Thesis defence.

Finally, I would like to acknowledge my supervisor, Dr. Rene V. Mayorga, for his support,

enlightenment, and guidance. The research completes because of his unlimited scientific

support, morals, and consistency in adding value to the work ethics for the researchers.

TABLE OF CONTENTS

ABSTRACT .................................................................................................................................... i

ACKNOWLEDGEMENTS ........................................................................................................ iii

POST DEFENCE ACKNOWLEDGEMENTS ....................................................................... iv

TABLE OF CONTENTS ............................................................................................................. v

LIST OF TABLES ....................................................................................................................... ix

LIST OF FIGURES ...................................................................................................................... x

LIST OF EQUATIONS.............................................................................................................. xii

LIST OF APPENDICES ........................................................................................................... xiii

LIST OF ABBREVIATIONS................................................................................................... xiv

1. INTRODUCTION ................................................................................................................ 1

Description of the Thesis ............................................................................................... 1

1.1.1 Research Issue .......................................................................................................... 1

1.1.2 Contribution to the Area of Research ..................................................................... 2

1.1.3 Objectives ................................................................................................................. 5

1.1.4 Methodology............................................................................................................. 6

1.1.5 General Structure.................................................................................................... 10

The Layout of the Thesis ............................................................................................. 13

2. LITERATURE REVIEW.................................................................................................. 14

Introduction ................................................................................................................... 14

Healthcare Systems and Intelligent Systems .............................................................. 18

Diabetes Mellitus and Intelligent Systems .................................................................. 22

3. DIABETES MELLITUS ................................................................................................... 36

Introduction of The Diabetes Mellitus ........................................................................ 36

3.1.1 History of Diabetes Mellitus.................................................................................. 40

3.1.2 Statistics of Diabetes Mellitus ............................................................................... 41

3.1.2.1 Prevalence of Diabetes Mellitus .................................................................... 42

3.1.2.2 Incidence of Diabetes Mellitus ...................................................................... 46

Types of Diabetes Mellitus .......................................................................................... 46

3.2.1 Prediabetes .............................................................................................................. 49

3.2.2 Type 1 Diabetes Mellitus ....................................................................................... 51

3.2.3 Type 2 Diabetes Mellitus ....................................................................................... 52

3.2.4 Gestational Diabetes Mellitus (GDM) .................................................................. 58

Symptoms of Diabetes Mellitus .................................................................................. 59

Diagnosis Methodologies of Diabetes Mellitus.......................................................... 60

3.4.1 Fasting Plasma Glucose (FPG).............................................................................. 60

3.4.2 2-Hour Plasma Glucose (2-h PG).......................................................................... 60

3.4.3 Hemoglobin A1C ................................................................................................... 61

3.4.4 Diagnostic Criteria ................................................................................................. 61

Challenges of Diagnosis Diabetes Mellitus ................................................................ 62

4. FUZZY SET THEORY ..................................................................................................... 63

Introduction of Fuzzy Set Theory................................................................................ 63

Fuzzy Set Theory .......................................................................................................... 64

4.2.1 Definition ................................................................................................................ 64

4.2.2 Basic Operations on Fuzzy Sets ............................................................................ 64

4.2.3 Membership Functions........................................................................................... 66

4.2.3.1 One Dimension Membership Functions........................................................ 66

4.2.3.1.1 Triangular Membership Functions ............................................................ 67

4.2.3.2 Two Dimensions Membership Functions ..................................................... 67

4.2.4 Fuzzy Relations ...................................................................................................... 67

4.2.5 Linguistic Variables ............................................................................................... 68

4.2.6 Fuzzy Conditional Statements ............................................................................... 69

4.2.7 Fuzzy Reasoning .................................................................................................... 69

Fuzzy Logic System ..................................................................................................... 70

4.3.1 Fuzzy Inference System ......................................................................................... 71

4.3.2 Mamdani Fuzzy Models ........................................................................................ 74

4.3.3 Fuzzification ........................................................................................................... 75

4.3.4 Knowledge Base ..................................................................................................... 75

4.3.5 Fuzzy Inference Engine ......................................................................................... 76

4.3.6 Defuzzification ....................................................................................................... 76

4.3.6.1 Centroid of Area ............................................................................................. 76

Application in MATLAB ............................................................................................. 77

4.4.1 The Fuzzy Logic Toolbox ..................................................................................... 77

4.4.2 The Fuzzy Logic Performance Evaluation ........................................................... 77

4.4.3 Application Designer ............................................................................................. 78

5. DESCRIPTION OF SYSTEM .......................................................................................... 79

Introduction ................................................................................................................... 79

The Overall System Operation Modes ........................................................................ 82

Definition of The System ............................................................................................. 83

5.3.1 The Primary Type II Diabetes Prognosis Fuzzy Inference System .................... 84

5.3.2 The Secondary Type II Diabetes Prognosis Fuzzy Inference System ................ 84

5.3.3 Personal Features Type II Diabetes Tendency Fuzzy Inference System ............ 86

5.3.4 Biological Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

5.3.5 Lifestyle Habits Type II Diabetes Mellitus Tendency Fuzzy Inference System 95

The Application Design of Type II Diabetes Mellitus Diagnosis System ................ 99

The System Results .................................................................................................... 104

5.5.1 Performance Evaluation of the System ............................................................... 104

5.5.2 Simulation and Analysis of Results .................................................................... 105

6. CONCLUSIONS ............................................................................................................... 111

System Review ........................................................................................................... 111

Main Contribution of the Thesis................................................................................ 112

Future Work ................................................................................................................ 113

REFERENCES .......................................................................................................................... 115

APPENDICES ........................................................................................................................... 136

LIST OF TABLES

Table 2-1: Healthcare Research Works and Accuracy Values.................................................. 16

Table 2-2: Diabetes Mellitus Research Works and Accuracy Values ...................................... 17

Table 3-1: Diagnosis Criteria for Prediabetes (Watson, 2017) ................................................. 50

Table 3-2: Risk Factors for Type 2 Diabetes Mellitus (Chamany & Tabaei, 2010) ................ 57

Table 5-1: The System Operation Modes ................................................................................... 82

Table 5-2: Genders Membership Functions’ Risk Levels ......................................................... 89

Table 5-3: Age Membership Functions Level ............................................................................ 89

Table 5-4: Family History Membership Functions’ Risk Levels.............................................. 89

Table 5-5: Nationality Membership Functions' Risk Levels ..................................................... 90

Table 5-6: Body Mass Index Level ............................................................................................. 90

Table 5-7: Blood Pressure Categories (American Heart Association, 2018) ........................... 93

Table 5-8: Blood Pressure's Membership Functions Limits...................................................... 94

Table 5-9: Cholesterol's Membership Functions Limits ............................................................ 94

Table 5-10: Blood Glucose Levels for Membership Function .................................................. 94

Table 5-11: Pregnancy Situation Membership Functions' Risk Levels .................................... 94

Table 5-12: The Categorization of Physical Activity Levels .................................................... 97

Table 5-13: The Categorization of Dietary Factors ................................................................... 98

Table 5-14: The Membership Functions of Smoking Habit ...................................................... 98

Table 5-15: The Membership Functions of Alcohol Consumption .......................................... 98

Table 5-16: Randomized Diabetes Dataset Performance Evaluation ..................................... 108

Table 5-17: Pima Indian Diabetes Dataset Performance Evaluation ...................................... 108

Table 5-18: BioStatistiscs Diabetes Dataset Performance Evaluation ................................... 108

LIST OF FIGURES

Figure 1.1: The Illustration of All of 5 Fuzzy Inference Systems .............................................. 9

Figure 1.2: The Structure of the System ..................................................................................... 12

Figure 3.1: The Top 10 Global Causes of Deaths in 2000 (World Health Organization (WHO),

2018) .............................................................................................................................................. 37

Figure 3.2: The Projections for 2010 and 2030 ......................................................................... 45

Figure 3.3: The Estimated Numbers for People with Diabetes Mellitus (Cho, et al., 2018) .. 45

Figure 3.4: Disorders of Glycemia: Etiologic Types and Stages (American Diabetes

Association, 2010) ........................................................................................................................ 48

Figure 3.5: Environment Impacts Possible Pathways for Type 2 Diabetes Mellitus (Dendup,

Feng, Clingan, & Astell-Burt, 2018) ........................................................................................... 55

Figure 3.6: Optimization of Existing Strategies for Treating Type 2 Diabetes Mellitus

(Chatterjee, Khunti, & Davies, 2017) .......................................................................................... 56

Figure 4.1: General Illustration of a Fuzzy Inference System .................................................. 73

Figure 5.1: The Diagnosis of Type II Diabetes Mellitus System's Flow Chart ....................... 80

Figure 5.2: Fuzzy Inference Systems.......................................................................................... 81

Figure 5.3: The Primary Fuzzy Inference System for the Diagnosis of Type II Diabetes Mellitus

........................................................................................................................................................ 85

Figure 5.4: The Secondary Fuzzy Inference System for the Diagnosis of Type II Diabetes

Mellitus .......................................................................................................................................... 85

Figure 5.5: Personal Features Type II Diabetes Mellitus Tendency......................................... 87

Figure 5.6: The Explanation of Family History for Type II Diabetes Mellitus Patients (Franks,

2010) .............................................................................................................................................. 87

Figure 5.7: Biological Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

........................................................................................................................................................ 93

Figure 5.8: Lifestyle Habits Type II Diabetes Mellitus Tendency Fuzzy Inference System .. 97

Figure 5.9: Welcome Page (MATLAB, The MathWorks Inc., 1994-2020) .......................... 101

Figure 5.10: Personal Features Screen (MATLAB, The MathWorks Inc., 1994-2020) ....... 101

Figure 5.11: Biological Features Screen (MATLAB, The MathWorks Inc., 1994-2020) .... 102

Figure 5.12: Question Screen (MATLAB, The MathWorks Inc., 1994-2020) .................... 102

Figure 5.13: Lifestyle Habits Screen (MATLAB, The MathWorks Inc., 1994-2020) .......... 103

Figure 5.14: Evaluation Screen (MATLAB, The MathWorks Inc., 1994-2020).................. 103

Figure 5.15: Dataset Comparison for The Proposed System .................................................. 107

LIST OF EQUATIONS

Equation 4.1 ................................................................................................................................... 64

Equation 4.2 ................................................................................................................................... 65

Equation 4.3 ................................................................................................................................... 65

Equation 4.4 ................................................................................................................................... 65

Equation 4.5 ................................................................................................................................... 65

Equation 4.6 ................................................................................................................................... 66

Equation 4.7 ................................................................................................................................... 66

Equation 4.8 ................................................................................................................................... 67

Equation 4.9 ................................................................................................................................... 67

Equation 4.10................................................................................................................................. 68

Equation 4.11................................................................................................................................. 68

Equation 4.12................................................................................................................................. 68

Equation 4.13................................................................................................................................. 76

Equation 4.14................................................................................................................................. 78

Equation 5.1 ................................................................................................................................. 104

Equation 5.2 ................................................................................................................................. 104

Equation 5.3 ................................................................................................................................. 105

Equation 5.4 ................................................................................................................................. 105

LIST OF APPENDICES

APPENDIX A: DIABETES MELLITUS ................................................................................ 136

APPENDIX B: FUZZY SET THEORY .................................................................................. 144

APPENDIX C: TESTING DATA ............................................................................................ 154

APPENDIX D: FUZZY INFERENCE SYSTEM ................................................................... 207

APPENDIX E: FUZZY LOGIC CODE................................................................................... 224

APPENDIX F: THE INTERFACE SOFTWARE PROGRAM IN MATLAB ..................... 262

LIST OF ABBREVIATIONS

Nomenclature

2hPG 2-Hour Plasma Glucose

A1C Glycated Hemoglobin A1C

ABC Artificial Bee Colony

ADA American Diabetes Association

ADH Antidiuretic Hormone

AFR Africa

AI Artificial Intelligence

ANFIS Adaptive Neuro-Fuzzy Inference Systems

ANN Artificial Neural Network

BFT Biological Features Tendency

BL Bivalent Logic

BMI Body Mass Index

CART Classification and Regression Tree

CBR Case-Base Reasoning

CDSSs Clinical Decision Support Systems

CS Classical Set

DCTT Diabetes Control and Complications Trial

DM Diabetes Mellitus

DMM Data Mining Methods

DSS Decision Support Systems

DT Decision Trees

EHR Electronic Health Records

EMME Eastern Mediterranean and the Middle East

ES Expert Systems

EUR Europe

FAHP Fuzzy Analytical Hierarchy Process

FCS Fuzzy Conditional Statement

FIS Fuzzy Inference Systems

FIS Fuzzy Inference System

FL Fuzzy Logic

FLT Fuzzy Logic Toolbox

FN False Negative

FP False Positive

FPG Fasting Plasma Glucose

FST Fuzzy Set Theory

GA Genetic Algorithm

GDM Gestational Diabetes Mellitus

GUI Graphical User Interface

GUIDE Graphic User Interface Development Environment

HDL High-Density Lipid

HIV Human Immunodeficiency Virus

IDF International Diabetes Federation

IFG Impaired Fasting Glucose

IGT Impaired Glucose Tolerance

IR Insulin Resistance

IS Intelligent Systems

KMC K-Means Clustering Algorithm

k-NN k-Nearest Neighbours Algorithm

LDL Low-Density Lipid

LHFT Lifestyle Habit Features Tendency

LM Levenberg-Marquardt Algorithm

MENA The Middle East and North Africa

MFIS Mamdani Fuzzy Inference System

MLR Multinomial Logistic Regression

MLT Machine Learning Techniques

NAC North America & Caribbean

NGSP National Glycohemoglobin Standardization Program

NO2 Nitrogen Dioxide

OGTT Oral Glucose Tolerance Test

PCBs Polychlorinated Biphenyls

PFT Personal Features Tendency

PM Particular Matter

RL Reinforcement Learning

RMSE Root Mean Square Error

RPD Randomized Produced Dataset

SCA/ SACA South and Central America

SEA South and East Asia

SFIS Sugeno Fuzzy Inference System

SVM Support Vector Machine Technique

T1DM Type 1 Diabetes Mellitus

T2DM Type 2 Diabetes Mellitus

T2FO Type-2 Fuzzy Ontology

T2FS Type-2 Fuzzy Sets

TG Triglycerides

TN True Negative

TP True Positive

TSKFIS Takagi-Sugeno-Kang Fuzzy Inference System

WC Waist Circumference

WHO World Health Organization

WHR Waist-to-Hip Ratio

WP Western Pacific

1. INTRODUCTION

Description of the Thesis

1.1.1 Research Issue

In the medical dictionary, diabetes mellitus defines “a chronic disease associated with

abnormally high levels of the sugar glucose in the blood” (Shiel, Medical Definition of Diabetes

Mellitus: MedicineNet, 2017). There are two significant reasons: inefficient insulin production

problems, such as pancreas production problems or lower blood glucose levels, and

insufficient sensitivity of cells to the action of insulin. Type 1 diabetes mellitus, type 2 diabetes

mellitus, and gestational diabetes mellitus are the types of diabetes. Today, different types of

blood glucose tests, such as a fasting plasma glucose (FPG), 2-hour plasma glucose (2HPG),

oral glucose tolerance test (OGTT) and hemoglobin A1C, use for diagnosis of diabetes mellitus.

Doctors or medical practitioners can diagnose that the patients' risk of suffering from diabetes

mellitus with the help of these tests.

The world is giving alarm about rapid increases in diabetes mellitus. In 2019, 463 million

people, who are between 20 years old and 79 years old, had diabetes mellitus (International

Diabetes Federation, 2019). Understandably, diabetes mellitus is one of the significant

problems for the world population. The United States of America Center for Disease Control

and Prevention Center predicts that people who suffer from diabetes mellitus have higher

expenses than non-diabetic people.

The most conventional way of diagnosing types of diabetes mellitus is through the detection of

these disease symptoms by specialized practitioners based on their blood glucose levels and

other factors. The diagnosis of disease decisions is related to medical practitioners' or doctors'

expertise level, experience, and perception. Recently, some partial automatic systems, which

can be replaced by the conventional methodologies for time and cost point of view, have been

developed to diagnose diabetes mellitus. Although these new systems save time and cost of

diagnosis, the needs of the medical practitioners are continuously necessary for verifying and

detecting patients' symptoms. Due to the patients’ symptoms variety, these needs have been

continuing, and their expertise is helping the new developments to diagnose quickly. Also,

human expertise will help easy the diagnosis of the disease in some conditions, such as diabetes

insipidus. Diabetes insipidus defines that “excessive urination and extreme thirst as a result of

the inadequate output of the pituitary hormone, antidiuretic hormone or vasopressin (ADH) or

the lack of the usual response by the kidney to antidiuretic hormone” (Shiel, Medical Definition

of Diabetes Insipidus: MedicineNet, 2018). As a simple explanation, medical practitioners or

doctors can suspect and determine the presence of several pathologies when analyzing the blood

glucose levels. The result of the analysis can be related to diabetes mellitus, such as gestational

diabetes mellitus, diabetes insipidus.

However, the replacement of human expertise with intelligent systems or new technologies in

medical diagnosis is a new approach that participates in technological developments. More

researchers are aware of the importance of intelligent systems or artificial intelligence

technologies use so that these systems can be more productive, cheaper and save time.

1.1.2 Contribution to the Area of Research

After new technological developments, expert systems have been used to solve many

complicated problems in the beginning step of intelligent systems development. Throughout

time, the numbers of Fuzzy Logic applications for clinical issues, such as automated diagnosis,

control systems, image processing, pattern recognition, etc., has increased gradually. It has

proven that Fuzzy Logic is a reliable tool for decision-making problems. Besides that, the Fuzzy

Set theory has been applied to many medical issues. The theory can define necessary

information in a linguistic language without any boundary, and it is better able to use human

expertise. Also, medical problems can be very complex, which means that many factors exist

to diagnose the disease. These factors may lead to uncertain clinical guidelines, such as lack of

information, non-specific data, probabilistic nature of data and outcomes vagueness in the

recommendations, strife, and fuzziness.

In analyzing the patients’ symptoms with conventional methodologies, the expertise of medical

practitioners or doctors is a significant need. As mentioned, some expert systems have launched

to save time and cost of diagnostic procedures. These systems include Machine Learning

Techniques, such as Artificial Neural Network, K-Means Clustering Algorithm, Support Vector

Machine (SVM) Technique, Data Mining Methods, Decision Trees, Decision Support Systems,

and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Some contributions that appear in the

area of diagnosis diabetes mellitus and diabetes mellitus include:

• The Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach has used for

creating an automatic diagnosis system of diabetes mellitus (Ubeyli, 2010). In this

approach, the author considered artificial neural network approaches and fuzzy set

theory together with using ANFIS. There are eight features as inputs, and the Pima

Indians Diabetes data set has used. The total accuracy of the methodology was

98.14% with the ANFIS model.

• Another essential part of diabetes mellitus is a feedback glucose control system. The

Fuzzy - Based Proportional - Integral Controller has used for modifying feedback

error (Ekram, Sun, Vahidi, Kwok, & Gopaluni, 2012). In this paper, the importance

of glucose levels control strategies analyzed and the evaluation strategy developed.

The results obtained from simulation illustrate the potential benefit of using a Fuzzy

Logic based proportional-integral controller for Type 2 diabetic patients.

• Using a distinct classification technique and Fuzzy Inference System has combined

to develop a system of diagnosis diabetes mellitus (Chandgude & Pawar, 2016). In

this paper, the proposed system diagnoses the types of diabetes and recommends

treatment for that type of diabetes. The system has trained and tested according to

the Pima Indians diabetes data set, and Graphical User Interface developed. Up to

900 training records generated, and the accuracy of the system up to 98%.

• The evaluation of real-world data is possible by using state of the art machine

learning algorithms (Mercaldo, Nardone, & Santone, 2017). The authors used the

Pima Indians diabetes data set and hypotheses. The hypotheses tested with Mann-

Whitney and Kolmogorov-Smirnov Test. J48, Multilayer Perceptron, Hoeffding

Tree, JRip, Bayes Net, and Random Forest are the algorithms, which have applied.

The total accuracy of their hypotheses is 0.757.

As mentioned above and Chapter 2, some researchers have used Fuzzy Set theory, Artificial

Neural Network and Adaptive Neuro-Fuzzy Inference System to be related to diabetes mellitus

disease. The proposed method has analyzed the information about patients’ diabetes mellitus

progress for developing diabetes mellitus diagnosis effectively and efficiently. However, in real

life, medical practitioners carry out this task. It is to hope that this developed intelligent system,

which is using by Fuzzy Logic, can more successfully diagnose the symptoms of diabetes

mellitus than medical practitioners.

The reasons behind choosing Fuzzy Logic are that: (1) Fuzzy Logic efficiently deals with

uncertainty and vagueness; and (2) Fuzzy Logic does not need any patient information (data)

for generating a diagnosis model. The drawbacks of Fuzzy Logic are: (1) the results of Fuzzy

Logic are generally based on fuzzy conditional statements, so sometimes it may give inaccurate

results; and (2) setting necessary fuzzy conditional statements and membership functions could

be a complicated task for the systems. However, except for Fuzzy Logic, other current

methodologies could not deal with uncertainty and vagueness. For example, Artificial Neural

Network, and Adaptive Neuro-Fuzzy Inference System, need a sufficient amount of patient

information for generating the diagnosis model. In contrast, Fuzzy Logic does not need any

patient information (data) for generating the diagnosis model.

1.1.3 Objectives

The initial perspective for this Thesis is to generate an intelligent system. The proposed study

will develop a new perspective for the diagnostic of Prediabetes and Type II Diabetes Mellitus.

Also, one of the primary benefits of the proposed research is to diagnose prediabetes, which has

not been to considered in other studies. Prediabetes is a stage before patients’ suffering from

Type II Diabetes Mellitus, and the early diagnosis of prediabetes will help reduce of prevalence

rate of Type II Diabetes Mellitus.

One of the missions of this Thesis is to apply an effective technology to the intelligent

diagnosing system and making the system more ideal. Some techniques, such as Artificial

Neural Network, Adaptive Neuro-Fuzzy Inference System, have chosen in other studies as an

intelligent system. However, to decide one powerful and useful technology for this particular

case is a complicated issue.

There are some significant objectives for this Thesis. The goals based on these targets and

design, and they can be summarized as follows:

• To illustrate that the diagnosis of prediabetes and type II diabetes mellitus is possible

by using an intelligent system apart from conventional methodologies.

• To assist the doctors for diagnosis of prediabetes and type II diabetes mellitus

practically and cost-effectively,

• To help to the reduction of undiagnosed or unaware patients who suffer from type

II diabetes mellitus,

• To display that Fuzzy Set theory could be easily used for diagnosis of type II

diabetes mellitus and complex health problems,

• To develop a user-friendly application that will interact with the intelligent system.

The user-friendly interface will help deeply people who do not know about medical

issues.

1.1.4 Methodology

The proposed methodology is to diagnose diabetes mellitus, which is mainly concentrating on

prediabetes or insulin resistance and type II diabetes mellitus patients, using a Fuzzy Inference

System (FIS). The detailed explanation of diabetes mellitus is presented in Chapter 3. The

proposed procedure shows the Intelligent Systems techniques implementation based on Fuzzy

Set theory/Fuzzy Logic, and the Mamdani Fuzzy Inference System approach. Chapter 4

acquaints the Fuzzy Set theory, Fuzzy Logic, and Mamdani Fuzzy Inference System.

Most of the intelligent systems obtain knowledge from datasets, and the systems train and test

with these datasets. Also, the number of datasets is another critical factor for most of the

intelligent systems. However, if there is no available enough data, the Fuzzy Logic is one of the

most effective methodologies for developing the intelligent system for any problem. One of the

benefits of Fuzzy Logic is to deal with vagueness and uncertainty efficiently.

Another important advantage of a Fuzzy Inference System is the use of linguistic language as

fuzzy variables to represent inputs and outputs. The relationship between fuzzy inputs and

outputs defined as fuzzy reasoning is known as approximate reasoning. Fuzzy Conditional

Statements (FCSs), which is if-then rules for this system, operates the system for Fuzzy Logic.

The relationship between inputs (that consider personal features tendency, biological features

tendency and lifestyle habit tendency), and output (that considers Primary System patient’s

health situation), is the result of the if-then rules in the Primary System. The amount of the

inputs is different for each sub-system. The number of the inputs is five, five, and four,

respectively, for the personal features’ tendency module; the biological features tendency

module; and the lifestyle habit tendency module. The inputs for all the three subsystems are

gender, age, family history, nationality, body-mass index (BMI), for personal features

tendency; systolic blood pressure, diastolic blood pressure, blood glucose level, cholesterol

level, pregnancy situation, for biological features tendency; and dietary factors, smoking habits,

alcohol consumption habits, physical activity level, for lifestyle habit tendency. The Fuzzy

Logic Toolbox assists the complete proceeding of the Fuzzy Inference System design,

simulation, and code generation in MATLAB (MATLAB, The MathWorks Inc, 1994-2020)

for implementation of the real-life applications. The user-friendly interface can be developed

by the Graphic User Interface Development Environment (GUIDE) module, or the App

Designer; both available in the MATLAB platform. The Fuzzy Inference System can connect

with both modules, and they are useful for developing the user-friendly program. Also, the

graphical user interface development environment can transfer to the app designer quickly. The

App Designer module is employed in this Thesis because the app designer allows using the

program without having MATLAB's central system.

The proposed Primary System is the combination of three sub Fuzzy Inference Systems, and

the inputs are personal features, biological features, and lifestyle habit features. Also, the

proposed Secondary System considers two sub Fuzzy Inference Systems, and biological

features and personal features are the inputs of the Secondary System. Figure 1.1 shows the

illustration of the all of 5 Fuzzy Inference System. The diagnosis system reflects the relationship

between inputs and the patient’s diagnostic result. Despite having some similarities with the

works mentioned in Section 1.1.2 and Chapter 2, this proposed methodology considers for the

first time some additional features, such as different inputs, outputs, and subsystems.

The main significance of the proposed overall system is to consider prediabetes and type 2

diabetes mellitus diagnostic at the same time. The novelty of the proposed system is to use

different and diverse inputs and outputs and to be easily applicable to real-life diagnostic

systems. Furthermore, the overall system is based on some novel approaches, which can display

visual information via a user-friendly interface.

Figure 1.1: The Illustration of all 5 Fuzzy Inference Systems

SUB FUZZY INFERENCE

SYSTEMS

CENTRAL FUZZY

INFERENCE SYSTEMS

PERSONAL FEATURES

DIABETES TENDENCY

FUZZY INFERENCE SYSTEM

BIOLOGICAL FEATURES

DIABETES TENDENCY

LIFESTYLE HABIT DIABETES

TENDENCY FUZZY

INFERENCE SYSTEM

PRIMARY PROGNOSIS

SECONDARY PROGNOSIS

Primary Diagnosis Fuzzy Inference System Secondary Diagnosis Fuzzy Inference System

1.1.5 General Structure

The proposed methodology consists of three significant phases, which are the system design,

the Fuzzy Inference System, and the user-friendly application design. The structure of the

proposed Intelligent System is displayed in Figure 1.2.

• System Design: An extensive literature review is done to facilitate the selection of

the inputs and outputs variables, their number, and the weights of the inputs and

outputs. This phase is also the beginning step of the development of the Fuzzy

Inference System.

• Fuzzy Inference System (FIS): The Fuzzy Logic process is represented by the Fuzzy

Inference System, which considers the given inputs and outputs. The system uses a

set of rules, which relates linguistic relationships between inputs and outputs. The

detailed discussion will hold in Chapter 5. In this Thesis, two central (the Primary

and the Secondary) Fuzzy Inference Systems, which comprise of several subs Fuzzy

Inference Systems, as in Figure 1.1, have been used. The sub Fuzzy Inference

Systems, which employ for diagnosis of diabetes mellitus, are personal features

tendency, biological features tendency and lifestyle habit tendency.

• User-Friendly Application (App Design): A user-friendly application helps to

illustrate all of the 5 developed Fuzzy Inference Systems practically and effectively.

Apart from application design, as previously mentioned the user-friendly interface

can be developed by the Graphic User Interface Development Environment

(GUIDE) module, or the App Designer in MATLAB module. But to use the GUIDE

module it is necessary to use MATLAB. On the other hand, the App Designer can

assist people, who do not know MATLAB, very well. Also, the main advantages of

using the App Designer are: to show the Fuzzy Inference System, to create visual

understandability, and to help the user with a system feedback mechanism. The

feedback mechanism provides inputs from users, and the system produces the result.

Figure 1.2: The Structure of the System

PHASE 2

INFERENCE

SYSTEM

PRIMARY

INFERENCE

SYSTEM

PERSONAL

FEATURES

TENDENCY

SYSTEM

BIOLOGICAL

FEATURES

TENDENCY

SYSTEM

LIFESTYLE

TENDENCY

SYSTEM

PHASE 3

FRIENDLY

APPLICATION

DESIGNING OF THE USER-FRIENDLY

APPLICATION AND TRIAL OF THE SYSTEM

PHASE 1

SYSTEM

DESIGN

PROBLEM RECOGNITION, OBJECTIVES AND

SCOPE DEFINITION, COMPLATING LITERATURE

REVIEW, DESIGNING OF THE SYSTEM AND,

NUMBERS AND WEIGHTS OF INPUTS AND

OUTPUT

SECONDARY

INFERENCE

SYSTEM

The Layout of the Thesis

There are six chapters in this Thesis. The summary of each chapter is in the following:

• The literature review of the related works is presented in Chapter 2. This chapter

also contains intelligent systems for healthcare problems, and for diabetes mellitus.

• Chapter 3 represents a review of the background analysis of diabetes mellitus, some

medical information, the rules for diagnosis, the procedure of determination, and the

cardinal symptoms of the diagnosis.

• The Fuzzy Set theory and the user-friendly application is discussed in Chapter 4.

The chapter includes Fuzzy Set theory, Fuzzy Logic, Membership Functions, Fuzzy

Conditional Statements, Fuzzy Inference System, Fuzzy Logic toolbox, and

designing the application.

• The structure of the Thesis, which comprises a Fuzzy Inference System and a User-

friendly application, is explained in Chapter 5.

• The Conclusions of this Thesis are presented in Chapter 6, which summarizes the

proposed methodology with benefits and drawbacks. It explains further studies with

the proposed methodology and the possible application of the proposed system.

2. LITERATURE REVIEW

Introduction

In the modern era, medical diseases related to heart, cancer, diabetes have become a

considerable burden for human-being. The diagnosis of diseases is time consuming and

expensive process. If the diseases do not diagnose on time, they have long-term side effects on

human-being physically or psychologically, and some diseases cause the early death of human

beings. For example, if the heart problem of the patient diagnosed early, the risk of heart attack

or cardiovascular problems reduces in the future.

With the development of technology, the researcher found out that the more straightforward

diagnosis of diseases could be possible. The statistical methodologies, intelligent systems and

optimization algorithms help the researcher to develop the diagnosis system and prediction of

the risk factor of suffering from the diseases with analyzing the existing patient data.

In this section, the past studies, which are related to intelligent systems for medical problems,

are discussed. The chapter consists of two different parts: healthcare systems and intelligent

systems and diabetes mellitus and intelligent systems. Healthcare systems and intelligent

systems have presented the examples of the developed intelligent systems for any disease, while

the examples about diabetes mellitus are in the section called diabetes mellitus and intelligent

systems.

There are limited numbers of developed Intelligent Systems for diagnostics of diabetes mellitus.

This limitation affects the research background of this Thesis study. But when all Intelligent

Systems for general diseases diagnostics are checked; some of these systems that can deal with

uncertainty can considered good candidates to diagnose Diabetes Mellitus.

Some of the mentioned articles are displayed in Table 2-1 and Table 2-2. Table 2-1 summarizes

some examples of healthcare articles, methodology, and accuracy rates, while the examples

diabetes mellitus articles, methodologies and accuracy rates are referred to in Table 2-2.

Table 2-1, presents studies published between 2010 and 2019, for general diseases . Mostly,

Fuzzy Logic, Fuzzy Inference System, Fuzzy Expert Systems, and Neural Networks have

chosen as a methodology. The accuracy range of the previous literature systems is between 70%

and 96.4%. These studies explained that when there are limited numbers of patient information

(data) or uncertain situations, Fuzzy Logic, Fuzzy Inference System, and Fuzzy Expert System

are some of the best possible methodologies to choose for diagnostic problems.

In particular, the Table 2-2 illustrates articles between 2010 and 2019, dealing with Diabetes

Mellitus. In general, machine learning methodologies, optimization algorithms, and Fuzzy

Logic have used as a methodology. The accuracy of the proposed systems was between 65%

and 99%. However, some methodologies did not have any calculated accuracy, and these

methodologies could not be compared with this Thesis research. These previous works

calculated the accuracy, sensitivity, or specificity of their methodologies; however, but these

attributes were not disclosed. The reason behind mentioning previous studies without accuracy

is that these studies help to decide inputs, outputs, and fuzzy conditional statements.

Table 2-1: Healthcare Research Works and Accuracy Values

AUTHORS METHODOLOGY ACCURACY

(Nazari, Fallah, Kazemipoor, & Salehipour,

2018) FAHP-FIS

26 out of 81

diagnosed

(Omisore, Samuel, & Atajeromavwo, 2017) FL – NN – GA

70% accuracy/ 60%

sensitivity

(Thakur, Raw, & Sharma, 2016) FIS 80%

(Chakraborty, Chakraborty, & Mukherjee,

Fuzzy C-Means

Clustering/ FIS

96.4% (C-Means)/

85.71% (subtractive)

(Samuel, Omisore, & Ojokoh, 2013) FL 94% accuracy

(Kadhim, Alam, & Kaur, 2011) FES 90% accuracy

(Adeli & Neshat, 2010) FES 94% accuracy

Table 2-2: Diabetes Mellitus Research Works and Accuracy Values

AUTHORS METHODOLOGY ACCURACY

(Shankar & Manikandan, 2019) Grey-Wolf Optimization 71% accuracy

(Abdullah, Fadil, & Khairunizam, 2018) FES N/A

(El-Sappagh, et al., 2018) FRBS N/A

(Abdelgader & Hagras, 2018) Type-2 FL N/A

(Benamina, Atmani, & Benbelkacem,

2018) FL

66% JColibri/

73% Weka tree

(Geman, Chiuchisan, & Toderean, 2017) ANFIS N/A

(Mansourypoor & Asadi, 2017) GA/ RL 84% - 99%

(Mahata, Mondal, Alam, & Roy, 2017) Mathematical Model N/A

(Ambilwade & Manza, 2016) FIS/ ML 91.16% accuracy

(Lukmanto & Irwansyah, 2015) FHM 87.46% accuracy

(Lalka & Jain, 2015) FES N/A

(Visalatchi, Gnanasoundhari, &

Balamurugan, 2014) Data Mining 86% - 74.8%

(Bashir, Qamar, Khan, & Javed, 2014) ID3/ C4.5/ CART N/A

(Kumar, Vijav, & Devaraj, 2013) Hybrid Colony/ FS 98.5% accuracy

(Beloufa & Chikh, 2013) A Bee Colony 97.82% - 98.66%

(Cosenza, 2012) FL DSS 65.2% accuracy

(Patra & Mondal, 2012) Trapezoidal Numbers N/A

(Lee & Wang, 2011) DSS 75% accuracy

(Ganji & Abadeh, 2010) Ant Colony Optimization 79.48% accuracy

(Tadic, Popovic, & Dukic, 2010) Fuzzy Approach N/A

Healthcare Systems and Intelligent Systems

Nazari, Fallah, Kazemipoor and Salehipour developed the clinical decision support system

for heart disease in the paper called ‘A Fuzzy Inference-Fuzzy Analytic Hierarchy Process-

Based Clinical Decision Support System for Diagnosis of Heart Disease’ (Nazari, Fallah,

Kazemipoor, & Salehipour, 2018). The system aims to calculate the likelihood of developing

heart disease. The developed system was based on the Fuzzy Analytic Hierarchy Process and

Fuzzy Inference System. The proposed system tested by the attendance of 100 real patients and

seven medical doctors. According to doctors’ observation, 81 patients needed to do further

investigation and test, and 20 patients out of 81 patients suffered from heart disease. However,

the proposed system found that 26 patients out of 81 patients, including the 20 patients, who

suffered from heart disease, need further investigations. The findings show that by using the

proposed support system, the cost and resources can save.

Omisore, Samuel and Atajeromavwo conducted a study related to tuberculosis diagnostic and

published ‘A Genetic-Neuro-Fuzzy Inferential Model for Diagnosis of Tuberculosis’ (Omisore,

Samuel, & Atajeromavwo, 2017). The proposed methodology used Fuzzy Logic, neural

network, and genetic algorithm together and developed a Genetic-Neuro-Fuzzy Inference

System. Twenty-four input variables and an output variable existed. The proposed system tested

by ten patient information. The performance evaluation of the proposed system completed with

sensitivity and accuracy, which are 60% and 70%, respectively. The drawback of the proposed

methodology is the less amount of data for performance evaluation. The system should evaluate

with more patient information.

Diagnostic system for Thalassemia is discussed in the paper ‘Design of A Fuzzy Model for

Thalassemia Disease Diagnosis: Using Mamdani Type Fuzzy Inference System’ by Thakur,

Raw and Sharma (Thakur, Raw, & Sharma, 2016). The aims of the proposed methodology are

that design a Fuzzy Inference System and diagnose the Thalassemia disease by using the Fuzzy

Logic. The proposed system consisted of 3 inputs, output and 15 fuzzy conditional statements.

The system’s performance evaluation completed by using 15 patients' information, and the

accuracy of the proposed techniques was roughly 80%. However, it is understandable that the

proposed system should test and train more patient information for reaching efficient results.

The detection of Parkinson's disease creates an enormous economic burden for countries

because of the disease diagnosed clinically, but the disease needs very sophisticated

investigations for laboratory confirmation. The paper, called ‘Detection of Parkinson’s Disease

Using Fuzzy Inference System’, is discussed a developed system for the detection of

Parkinson's disease by Chakraborty, Chakraborty and Mukherjee (Chakraborty, Chakraborty,

& Mukherjee, 2016). The proposed methodology used the Fuzzy C-Means Clustering algorithm

for developing Sugeno-Takagi Fuzzy Inference System, and sixteen attributes and outputs have

used. Two different systems have tested in the proposed study. The performance evaluation

values for the systems were up to 96.4% for Fuzzy C-Means based Fuzzy Inference System

results and 85.71% for subtractive clustering-based Fuzzy Inference System. The proposed

methodologies’ performance was better than previous studies in the literature, and C-Means

Clustering gave an outstanding result than subtractive clustering.

Samuel, Omisore and Ojokoh proposed the research about typhoid fever, and the research was

published in the paper, called ‘A Web-Based Decision Support System Driven by Fuzzy Logic

for The Diagnosis of Typhoid Fever’ (Samuel, Omisore, & Ojokoh, 2013). The elements of the

proposed system were the knowledge base and a Fuzzy Inference System. The proposed system

can run on the Internet smoothly. Fifteen diagnoses variables, related to patient history, patient

physical examination and laboratory investigation, used in the proposed techniques. 94%

accuracy has found in the proposed system, and the rate shows that the proposed system was

efficient and useful for the diagnosis of typhoid fever. The benefit of the proposed method was

reachable quickly from any location by using technology.

Sikchi, Sikchi and Ali published a paper called ‘Fuzzy Expert Systems (FES) for Medical

Diagnosis’, about the trends of Fuzzy Expert Systems usage with medical diagnosis (Sikchi,

Sikchi, & Ali, 2013). The paper focused on a total of 173 articles from 124 journals, several

proceedings and web media. The classes of the articles were reviews and surveys on Fuzzy

Expert Systems on medical diagnosis, methodologies and modelling of Fuzzy Expert Systems,

applications of Fuzzy Expert Systems in medical diagnosis, applications of Fuzzy Expert

Systems in medical diagnosis, Neuro-Fuzzy Approaches, Fuzzy Expert System shells and

frameworks. Approximately 62% of previous studies were between 2004 and 2011. The

increased volume of last year illustrated the effect of technological development in medical

diagnosis.

Anooj presented a clinical decision support system for computer-aided diagnosis of heart

disease in the paper called ‘Clinical Decision Support System: Risk Level Prediction of Heart

Disease Using Weighted Fuzzy Rules’ (Anooj, 2012). The proposed system consisted of

creating two steps: weighted fuzzy rules and a fuzzy rule-based decision support system. The

risk level of heart disease predicted by the utilization of biomarkers or attributes of the proposed

system, which were age, sex, total cholesterol level, HDL, LDL, smoking status, hypertension,

and pre-eclampsia. The performance evaluated by a dataset from Cleveland, Hungarian and

Switzerland heart disease dataset. The proposed techniques compared with the neural network-

based approach and the fuzzy-based system, and the accuracy was 10% higher than the fuzzy-

based system and 20% more than the neural network-based approach.

The intelligent diagnosis system for the disease of the Human Immunodeficiency Virus (HIV)

was developed by Imianvan, Anosike and Obi (Imianvan, Anosike, & Obi, 2011). The

proposed system used the Fuzzy Cluster Means Algorithm. The proposed technique consisted

of the knowledge base, Fuzzy C-Means Inference System, and decision support system. There

were thirteen basic and significant parameters in the proposed system for the diagnosis of HIV.

The proposed system proved that the Fuzzy Cluster Means Algorithm is a more natural and

intelligent methodology of classification and matching symptoms of HIV.

Kadhim, Alam and Kaur proposed an expert system for back pain diagnosis and the proposed

expert system published in the paper, called ‘Design and Implementation of Fuzzy Expert

System for Back Pain Diagnosis’ (Kadhim, Alam, & Kaur, 2011). The proposed Fuzzy Expert

System used body mass index, age and gender of patients, the clinical observation symptoms

as input parameters, and the system diagnosed the back-pain disease and gave the treatment

advice for the patients. The system evaluation completed with twenty patients, who were

thirteen males and seven females and the system accuracy was 90%. The paper gave

explanations about the designed user interface and advice in the interface.

The overview of the intelligent tools in health care systems and the review of challenges in the

field was explained in the paper, ‘Intelligent and Expert Systems in Medicine – A Review’, by

Schatz and Schneider (Schatz & Schneider, 2011). Researches about artificial intelligence in

medicine started at the end of the 1960s. Some examples of the developments are CASNET

(1960), DENDRAL (1967), INTERNIST (1974), MYCIN (1976), EXPERT (1979),

ONCOCIN (1981) and more. Apart from artificial intelligence, intelligent agent and multiagent

systems has used in previous studies. Also, the study shows that smart hospitals become

widespread.

Adeli and Neshat conducted a study related to heart disease and published a paper, ‘A Fuzzy

Expert System for Heart Disease Diagnosis’ (Adeli & Neshat, 2010). In the design of the Fuzzy

Expert System, Mamdani Fuzzy Inference System, 13 inputs (chest pain type, blood pressure,

cholesterol, resting blood sugar, maximum heart rate, resting electrocardiography, exercise, old

peak (ST depression-induced), thallium scan, sex, and age) and 44 fuzzy conditional statements

have used. Before deciding the number of fuzzy conditional statements, the trial of probable

rules has completed. The possibilities were 64 rules, 15 rules, 10 rules and 5 rules. The system

was based on the V. A. Medical Center, Long Beach and Cleveland Clinic Foundation database.

The accuracy of the proposed methodology was 94%. Also, the system gave better results than

non-expert urologists.

Diabetes Mellitus and Intelligent Systems

Shankar and Manikandan explored the diagnosis of diabetes mellitus diseases using an

optimized fuzzy rule set by grey wolf optimization (Shankar & Manikandan, 2019). In this

work, the Pima Indians Dataset used for diabetes prediction. The dataset comprised of only

female patients. A total of 17 fuzzy rules was produced by using eight features and two classes

from the Pima Indians Data Set. The optimal rules for output were provided by using the grey

wolf optimization algorithm. The algorithm took the fuzzy rules and created the optimal rules.

Accuracy, precision and recall metrics used for performance evaluation of the model. “The base

model worked on the concept of Ant Colony Optimization and fuzzy rule, which does not

provide sufficient accuracy because the algorithm optimizes the local features only and gives

71% accuracy” (Shankar & Manikandan, 2019). The drawback of the study is to use the same

data set as other studies. However, the proposed model based on grey wolf optimization gave

higher accuracy.

Abdullah, Fadil and Khairunizam developed a Fuzzy Expert System for the diagnosis of

diabetes mellitus (Abdullah, Fadil, & Khairunizam, 2018). In this study, the expert system used

for risk estimation of diabetes mellitus. In the Fuzzy Expert System, there were 17 inputs and

six outputs variables. The inputs were age, body-mass index (BMI), systolic blood pressure,

diastolic blood pressure, waist circumference (WC) for male and female, waist-to-hip ratio

(WHR) for male and female, cigarettes intake, exercise, alcohol, glycated hemoglobin

(HBA1C), triglycerides (TG), high-density lipid (HDL), low-density lipid (LDL), glucose level,

education level. The type of membership function for each input and output has been triangular.

The center of gravity has used for defuzzification methodology. The six risk categories were

very low (0%-10%), low (9%-20%), medium (19%-40%), high (39%-60%), very high (59%-

80%), very-very high (79%-100%). If the patient’s risk category level is between 79% and

100%, the result shows the confirmation of diabetes mellitus in the patient. However, the

calculation of accuracy has not done in the study. The lack of performance evaluation creates a

barrier to implement the developed system in real-life cases.

El-Sappagh et al. proposed a semantically intelligent hierarchical FRBS for diabetes mellitus

diagnosis (El-Sappagh, et al., 2018). The study helped clinical decision support systems

(CDSSs) for diabetes mellitus. Ontology and Fuzzy Logic in a novel manner combined in the

proposed study. The system had two different layers; the patient’s risk level determination and

the Mamdani min-max inference mechanism. There were 39 inputs, and types of membership

functions were triangular and trapezoidal. The output variable depended on the patient’s health

situation: diabetic and non-diabetic. The system tested 60 patients, which distribute as 53%

diabetic and 47% non-diabetic. The proposed system difference was to use a complete list of

diabetes mellitus’ attributes, including never used features in a similar type of system. The

proposed system used real cases for producing accurate results different from other studies.

Abdelgader and Hagras presented a diabetes mellitus diet recommendation system in the paper

called “Towards Developing Type 2 Fuzzy Logic Diet Recommendation System for Diabetes”

(Abdelgader & Hagras, 2018). The control of diabetes mellitus depended on medications,

regular exercise, and a balanced diet. Although various techniques have used for diabetes diet

control, the new technique need, which dealt with uncertain conditions, is continuing. In the

study, Abdelgader and Hagras aim to generate a white box artificial intelligence model which

should generate from data models which could be easily analyzed and interpreted by diabetes

patients and dietitian (Abdelgader & Hagras, 2018). The proposed system needed more than

one parameter (age, gender, weight, height, and activity level) for creating the best diet. The

reason behind using Type-2 Fuzzy Logic Systems is that it can deal with uncertainty, noise, and

imprecision. The personalized diet results were one of the biggest challenges in the research.

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic have conducted by Benamina,

Atmani and Benbelkacem. The primary aim of the proposed methodology is to improve the

accuracy of diabetes mellitus classification (Benamina, Atmani, & Benbelkacem, 2018). Also,

the paper aims to show the importance of a Fuzzy Inference System guided by data mining in

case-based reasoning modelling. The proposed methodology was comprised of two parts; “the

modelling part fuzzy realized by Fispro and the reasoning part realized by the platform JColibri”

(Benamina, Atmani, & Benbelkacem, 2018). The reason behind using Fuzzy Logic is to reduce

the complication of the degree of similarity calculation that can exist between individuals who

require different monitoring plans. The comparison between proposed methodology and other

techniques, which are k-nearest neighbours, decision tree and proposed decision tree, shows

that the proposed methodology’s accuracy was higher than other techniques on the same cases.

Accuracies were 66% for JColibri k-nearest neighbours, 73% for Weka decision tree and 81%

for Fispro fuzzy decision tree. One of the drawbacks of the proposed methodology is the

complicated diagnosis domain for diabetes mellitus.

The Pima Indian Diabetes Dataset has used the paper called Application of Adaptive Neuro-

Fuzzy Inference System for Diabetes Classification and Prediction by Geman, Chiuchisan and

Toderean (Geman, Chiuchisan, & Toderean, 2017). The methodology of research is the

Adaptive Neuro-Fuzzy Inference System (ANFIS), which is a combination of an adaptive

neural network and Takagi-Sugeno type fuzzy system. The risk factors for diabetes mellitus

used plasma glucose concentration a 2-hours in an oral glucose tolerance test, diastolic blood

pressure, triceps skinfold thickness, 2-hour serum insulin, body mass index and diabetes

pedigree function. The training data set was 80% of data, and 20% of the data set was the testing

data in the paper. The proving accuracy for the database was 85.35% for training data and

84.27% for testing data. The proposed system’s benefits are the fuzzy rules reduction, the

impact on memory and the implementation of the proposed structure.

Mansourypoor and Asadi have developed a diagnosis system for diabetes mellitus and

published the name of “Development of A Reinforcement Learning-Based Evolutionary Fuzzy

Rule-Based System for Diabetes Diagnosis” (Mansourypoor & Asadi, 2017). The proposed

model evaluated by using two different databases, which are the Pima Indian Dataset and

BioSat Diabetes Dataset. The proposed model comprised a two-step process; “(1) reducing the

number of rules and conditions and (2) using the Genetic Algorithm (GA) and Reinforcement

Learning (RL) to increase the consistency among the rules” (Mansourypoor & Asadi, 2017).

The first step presented rule learning, rule pruning, pruning rule antecedents and evolutionary

rule selection. The rule base building process’ numbers of rules were 7593 for Pima Indian

Diabetes and 10530 for BioSat Diabetes Dataset in the rule learning step, selecting 200 rules

from each dataset in the rule pruning step, 140 rules for Pima Indian Dataset and 136 rules for

BioSat Diabetes Dataset in the pruning rule antecedents step, and 19 rules for Pima Indian

Dataset and six rules for BioSat Diabetes Dataset in the evolutionary rule selection step. Also,

the second step displayed evolutionary rule tuning, adjusting weights and rule stretching. After

the implementation of the steps, the accuracy of the proposed system increased. The proposed

model gave higher accuracy than other methodologies for both datasets, and the accuracy was

84% for Pima Indian Diabetes Dataset and 99% for BioSat Diabetes Dataset.

Mahata, Mondal, Alam and Roy developed the mathematical model in the fuzzy and crisp

environment, and the model published in the ‘Mathematical Model of Glucose-Insulin

Regulatory System on Diabetes Mellitus in Fuzzy and Crisp Environment (Mahata, Mondal,

Alam, & Roy, 2017). The model solved with numerical results for both cases. Hukuhara

derivative concept was used to explain the fuzzy solution in the model. The model variables

were the plasma glucose concentration at time t, the generalized insulin variable for the remote

compartment at time t, the plasma insulin concentration at time t, the basal pre-injection value

of plasma-glucose, insulin-independent rate constant of glucose rate uptake in muscles, liver

and adipose tissue, the rate of decrease in tissue glucose uptake ability, the insulin-independent

increase in glucose uptake ability in tissue per unit of insulin concentration, the rate of the

pancreatic beta cells release of insulin after the glucose injection and with glucose

concentration, the threshold value of glucose above which the pancreatic beta-cells release

insulin, and the first-order decay rate for insulin in plasma pancreatic beta-cells release insulin.

Ambilwade and Manza solved the prognosis of diabetes mellitus by using the Fuzzy Inference

System and Multilayer Perception (Ambilwade & Manza, 2016). In the proposed system, the

Fuzzy Inference System used for predicting the initial risk of prediabetes and type 2 diabetes

mellitus using blood tests to measure the sugar/glucose levels in different situations. In the

proposed system, three hundred eighty-five patients’ information has used, and the dataset

collected from Diabetes Care and Research Center, Pune. The performance evaluation criteria

of the system were accuracy, sensitivity and specificity, and the results were 91.16% for

accuracy, 91.3% for sensitivity and 94.6% for specificity. One of the drawbacks of the proposed

system is the lack of patients’ data. If more patients’ data exists, the performance evaluation

results can change, and the system will give a better solution in future implementation.

The Fuzzy Hierarchical Model has used for the development of the early detection of diabetes

mellitus in the paper, ‘The Early Detection of Diabetes Mellitus (DM) Using Fuzzy Hierarchical

Model,’ by Lukmanto and Irwansyah (Lukmanto & Irwansyah, 2015). The proposed model

was the computational intelligence application by the usage of the Fuzzy Hierarchical Model

that can detect diabetes mellitus early. The designed model architecture was based on how the

doctors’ decision-making system works against potential disease risk, and the proposed model

has justified with the real patient data token from the laboratory. The proposed model used three

symptoms, polyuria, polydipsia and polyphagia, fasting blood glucose, 2-hour postprandial

blood glucose and age as an input, and the system output can be related to the potential risk of

diabetes mellitus. Five Fuzzy Inference Systems used nine rules in each system. The accuracy

of the proposed model was 87.46%. According to data, if the patient has diabetes mellitus, but

the age of the patient is not in the potential risk groups, the result of the proposed system will

be the only potential against diabetes mellitus.

Lalka and Jain published a ‘Fuzzy Based Expert System for Diabetes Diagnosis and Insulin

Dosage Control’ (Lalka & Jain, 2015). The method dealt with uncertainty and vagueness about

type 1 diabetes mellitus diagnosis. The inputs were body mass index (BMI), plasma glucose

level, minimum blood pressure and serum insulin level for diagnostic of type 1 diabetes

mellitus, and plasma glucose level and body mass index also used for insulin dosage control.

The membership function type is trapezoidal, and the defuzzification method of the system is

the centroid area. Also, there were 60 rules in the system. JAVA programming language used

for expert system design. The system verified with real-time patient results. It proves that the

effective and efficient diagnosis of type 1 diabetes mellitus is possible with the usage of the

proposed system.

The performance of data mining algorithms is an essential factor in choosing the best

methodology for the development of a suitable intelligent system. Visalatchi, Gnanasoundhari

and Balamurugan surveyed to select the better data mining techniques for diabetes mellitus

(Visalatchi, Gnanasoundhari, & Balamurugan, 2014). In the paper, five data mining algorithms

performance evaluated. The chosen data mining techniques were the C4.5 algorithm, the k-

nearest neighbour algorithm, naïve Bayes algorithm, support vector machines and the apriori

algorithm. The data source of the study was the Pima Indians Diabetes Database, and there were

nine different attributes (pregnancy, plasma, pres, skin, insulin, mass, pedi, age and class). The

accuracy of each algorithm helped to evaluate the system's performance. The analysis results

were 86%, 78%, 75%, 75% and 74.8%, C4.5 algorithm, k-nearest neighbour algorithm, naïve

Bayes algorithm, apriori algorithm and support vector machine algorithm, respectively. The

analysis showed that the C4.5 algorithm classifies diabetes mellitus better than other algorithms.

The efficient classification of diabetes mellitus can do with using multiple ensemble

classification techniques. Bashir, Qamar, Khan and Javed presented a study, called ‘An

Efficient Rule-Based Classification of Diabetes Using ID3, C4.5 & CART Ensembles’ (Bashir,

Qamar, Khan, & Javed, 2014). The primary aim of the proposed methodology was to find the

best ensemble techniques for decision trees. The used ensembles were Majority Voting,

Adaboost, Bayesian Boosting, Stacking and Bagging. The techniques evaluated by using

methodologies such as accuracy, sensitivity, specificity, and f-measure with taking advantage

of the Pima Indian Diabetes Dataset and BioStat Diabetes Dataset. Model building, learning,

and testing completed using RapidMiner5 Machine Learning Toolbox. The training set was

90% of data, while the testing set covered 10% of data. The bagging approach gave better

performance results than the other approaches. The accuracy of the bagging approach was the

highest for both datasets.

Kumar, Vijav and Devaraj used microarray data in the ‘A Hybrid Colony Fuzzy System for

Analyzing Diabetes Microarray Data’ (Kumar, Vijav, & Devaraj, 2013). Ant colony

optimization and artificial bee colony algorithms combined for analysis of the datasets, and it

was the strength of the proposed system. The optimal ruleset created by using ant colony

optimization and a total of 4 optimal rules existed. The accuracy of the optimal rules was 98.5%.

The utilization of the artificial bee colony algorithm was membership functions’ points in the

hybrid system. The proposed system gave more compact, accurate, and interpretable results

than other studies, such as genetic swarm algorithm.

Artificial Bee Colony algorithm has developed according to bee’s behaviours in nature. Beloufa

and Chikh generated a fuzzy classifier for diabetes mellitus (Beloufa & Chikh, 2013). The

proposed study used a modified artificial bee colony algorithm. The reason behind using a

modified artificial bee colony algorithm was to improve the diagnosis of diabetes mellitus

performance. Pima Indian Diabetes Dataset has used for evaluation of the system. The

performance evaluation criteria of the proposed methodology were classification rate,

sensitivity as specificity values. The classification rate of the proposed techniques was 84.21%.

The rate was higher than in previous studies in the literature. Also, the proposed methodology

tried with two more different diseases’ datasets. The accuracy of the trials was 97.82% and

98.66% for breast cancer and IRIS databases, respectively.

‘Off-line Control of The Postprandial Glycemia in Type 1 Diabetes Patients by A Fuzzy Logic

Decision Support’ was presented by Cosenza (Cosenza, 2012). The aim is to define fuzzy

techniques usage in the development of a decision support system. The decision support system

helped postprandial glycemia optimization for type 1 diabetes mellitus patients. The proposed

system contained three Fuzzy Inference System and an Adaptive Neuro-Fuzzy Inference

System. The inputs of the Fuzzy Inference Systems were type and the amount of food eaten

during the meal, converted amounts of carbohydrates, proteins and lipids, the pre-prandial

glycemia, the number of rapid insulin units recommended by the doctors and index number

(VAI), gender-specific, triglycerides, high-density lipoprotein (HDL) and body mass index, and

the output of the proposed system was the optimal number of insulin units. The test of the

proposed system completed by a set of 158 cases (120 for the training and 38 for testing the

system). The performance of the system was 65.2%.

Patra and Mondal presented the risk assessment using generalized trapezoidal numbers in the

paper called ‘Risk Analysis in Diabetes Prediction Based on A New Approach of Ranking of

Generalized Trapezoidal Fuzzy Numbers’ (Patra & Mondal, 2012). The study used the new

concept of trapezoidal numbers, and the proposed methodology overcame the previous

literature studies' disadvantages. In the case of type 2 diabetes mellitus risk analysis, the five

patients’ information, which is age, body weight, family history of diabetes mellitus, high blood

triglycerides level and hypertension, collected linguistically. The patients’ features were

different from each other. The risk calculated by using the Schmucker formula. After

completing all calculations, the patients’ risk level for suffering from diabetes mellitus has

attained, and patient five has had the highest risk level. Although the proposed model gives a

better result than existing techniques, the system has to try with more cases and different areas

for reaching a more accurate result in future usage.

Decision support systems and applications are an essential helper to decide effectively and

efficiently for doctors and medical practitioners. Lee and Wang developed a diabetes mellitus

decision support application by using a Fuzzy Expert System (Lee & Wang, 2011). The focus

of the paper was a novel Fuzzy Expert System. The system included a novel five-layer fuzzy

ontology (a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy

personal relation layer, fuzzy personal domain layer), fuzzy concepts and fuzzy relations for

diabetes mellitus application. The proposed system examined with the Pima Indians Diabetes

Database. C++ Builder 2007 programming language has used for the development of the Fuzzy

Expert System. The proposed methodology gave the best results for an age group slightly old

and slightly young, 91.2% and 90.3%, respectively. However, the accuracy rate for very very

young, very young and more or less young classes was higher than 75%. The uncertainties of

the proposed techniques depended on the dataset, dataset domain changes effect, and

fuzzification methods were testing. One of the drawbacks of the system is that when the dataset

changes happen, the fuzzy rules redesign can be necessary.

Ganji and Abadeh published a paper called ‘Using Fuzzy Ant Colony Optimization for the

Diagnosis of Diabetes Disease’ (Ganji & Abadeh, 2010). The study’s objective was ant colony

optimization utilization for extracting system rules set. The proposed system tested with Pima

Indian Diabetes Dataset. The proposed system gave better accuracy than previous studies.

Artificial ants have used for exploring the training search space and making candidate rules

gradually. The reason behind good comprehensibility was that the algorithm produces fewer

rules in a short time frame. The difference was that the proposed methodology learns rules for

each class separately. The accuracy of the proposed system was 79.48%. The benefits of the

proposed system are to use a new framework and improve the quality of rules, but one of the

proposed system’s drawbacks is not to consider all factors for diabetes mellitus.

The treatment process of diabetes mellitus is another critical element after the diagnosis process.

Tadic, Popovic and Dukic conducted and published their study, called ‘A Fuzzy Approach To

Evaluation and Management of Therapeutic Procedure in Diabetes Mellitus Treatment’ (Tadic,

Popovic, & Dukic, 2010), to determine the optimal therapy for patients. The therapy of diabetes

mellitus can vary according to patients’ health conditions, fasting blood glucose level, HbAC1,

body mass index and how many years they are suffering from the disease. The patients’ health

condition can be an uncertain factor in the proposed system. According to clinical guidelines,

many therapeutic methodologies existed, such as metformin therapy, insulin, DPP IV inhibitors

or sulfonylureas. Also, drug evaluation depended on unit price, efficiency, and side effect of

the drug. The proposed model has experimented with 3344 diabetic patients in the Internal

Clinic Center in Kragujevac, Serbia. The optimal therapeutic procedure for diabetes mellitus

therapy is Metformin and Sulfonylureas. Also, the result’s difference between combination

therapy of Metformin, Sulfonylureas and Insulin and Metformin and Sulfonylureas is

negligible, and the optimality level of both therapies is roughly equal.

Lee, Wang and Hangras published a paper called ‘A Type-2 Fuzzy Ontology and Its

Application to Personal Diabetic-Diet Recommendation’ (Lee, Wang, & Hagras, 2010). The

proposed methodology implemented by using the Borland C++ Builder programming language.

The proposed system consisted of personal profiles of patients and daily eaten items. Age, sex,

height, weight, and nutrition facts of common Taiwanese foods were the inputs of the proposed

system. The output of the proposed system was the recommended diabetic diet for each person.

The system tested by collecting data from 8 patients in the last five months. The experimental

results proved that the proposed system provided with a balanced menu for every diabetic

patient. However, the results of the proposed system are subjective.

The comparison between adaptive Neuro-Fuzzy Inference Systems and Multinomial Logistic

Regression was made in the paper ‘Diagnosis of Diabetes by Using Adaptive Neuro-Fuzzy

Inference Systems’ written by Karahoca, Karahoca and Kara (Karahoca, Karahoca, & Kara,

2009). The methodology aims to help patients who have diabetes mellitus but do not visit

doctors regularly or are undiagnosed. The dataset consisted of 470 subjects and four variables

(age, gender, frame (waist/hip ratio) and total cholesterol). The numbers of training and testing

sets were 300 patients and 90 patients, respectively. The result of the benchmarking displayed

that ANFIS is better than MLR for the diagnosis of diabetes mellitus. In ANFIS, the total epoch

number was just two while 300 epochs for training and 90 epochs for testing were in MLR.

Although the root means square error for training data set result was similar for each

methodology, the ANFIS’ testing error was lower than MLR’s error. The study proved that

ANFIS is better and faster learning techniques than MLR.

Temurtas, Yumusak and Temurtas published ‘A Comparative Study on Diabetes Disease

Diagnosis Using Neural Networks’ (Temurtas, Yumusak, & Temurtas, 2009). The study

presented a comparison between two different methodologies: multilayer neural network

structure with the Levenberg-Marquardt algorithm and a probabilistic neural network structure.

The performance evaluated by using the conventional validation and 10-fold cross-validation

techniques with the Pima Indian Diabetes Dataset. The paper clearly showed that both

evaluation techniques gave a similar accuracy, and the accuracy of methodologies was 82.37%

for multilayer neural networks with the Levenberg-Marquardt algorithm and 78.05% for the

probabilistic neural network. The paper proved that Multilayer Neural Network with the

Levenberg-Marquardt algorithm reached better accuracy than previous studies in the literature.

Usage of fuzzy integral for diagnosis of gestational diabetes mellitus displayed a paper name

was ‘Fuzzy Integral be Applied to The Diagnosis of Gestational Diabetes Mellitus by Zhang,

Song and Wu (Zhang, Song, & Wu, 2009). The Pima Indian Diabetes Dataset used for the

proposed system evaluation. Although the dataset had eight attributes and 768 different patient

data, five attributes (pregnancy times, plasma glucose concentration a 2 hours in an oral glucose

tolerance test, body mass index, diabetes pedigree function and age), and 250 patient data (200

for training and 50 for testing) were used in the proposed study. The proposed study tried BP

neural network with simulated annealing algorithm, and the trial’s accuracy was 75%. After

finding lower accuracy, the Sugeno Integral methodology implemented, and the accuracy of

Sugeno integral was higher than the BP neural network with simulated annealing algorithm.

The new accuracy was 82%. The proposed methodology’s drawback was the calculation of

local optimum in the BP neural network.

The paper, ‘Ontology-Based Intelligent Fuzzy Agent for Diabetes Application’, explained the

application of a three-layer fuzzy ontology model for the diabetes mellitus domain (Lee, Wang,

Acampora, Loia, & Hsu, 2009). The study was conducted by Lee, Wang, Acampora, Loia and

Hsu. The proposed study showed the application of Fuzzy Logic principles and ontology

techniques together for the diabetes mellitus decision-making process. The C++ Builder 2007

programming language and the Pima Indian Diabetes Dataset were used in the proposed model.

The performance evaluation of the proposed system completed by using the accuracy rate,

which was 91.2%.

Sharifi, Vosolipour, Aliyari Sh and Teshnehlab conducted a study and published a paper,

called ‘Hierarchical Takagi-Sugeno Type Fuzzy System for Diabetes Mellitus Forecasting’

(Sharifi, Vosolipour, Aliyari Sh, & Teshnehlab, 2008). The new methodology, which name was

a group method of data handling method based on the Adaptive Neuro-Fuzzy Inference System,

developed in the study. Also, the Pima Indian Diabetes Database used. The comparison between

the proposed methodology and Multilayer Perception, Radial Basis Network, Adaptive Neuro-

Fuzzy Inference System structure completed for four and eight features in the proposed

methodology. The proposed method’s accuracy was higher for both four features and eight

features.

3. DIABETES MELLITUS

Introduction of The Diabetes Mellitus

In the present day, the lifestyle of human being has changed, and some serious changes in

human life have led to the development of various diseases. People have started to live faster

and timelessly. This new faster and timeless lifestyle has brought some consequences to human-

beings, such as eating less healthy foods and more fast-food products or having less time to do

physical activities or spending less time for resting. The changes in lifestyle have opened a door

to fatal disease developments. According to the World Health Organization (World Health

Organization (WHO), 2018), in 2016 56.9 million people died worldwide, and approximately

55% of people were dead because of the top 10 causes.

Ischaemic heart disease and stroke have a fatal impact globally and are the leaders among the

other diseases for approximately the last two decades. In addition to the mentioned diseases,

diabetes mellitus is becoming one of the fatal chronic diseases. According to the statistics,

diabetes mellitus killed approximately 1.6 million people in 2016, and it was less than 1 million

in 2000 (World Health Organization (WHO), 2018). The top 10 causes of death for 2000, 2010

and 2016 displays in Figure 3.1. The graph shows the last 16 years’ top 10 health burdens, and

diabetes mellitus becomes a chronic disease for humankind and the important changes in

diabetes mellitus have been shown in a given time frame. Diabetes mellitus is also an economic

burden for governments. The treatment process and diagnosis process of diabetes mellitus are

quite expensive for governments. Also, the disease has some indirect effects, such as lost

workdays, productivity reduction and premature deaths, and costs, such as the money spent for

complications and extra treatments.

Figure 3.1: The Top 10 Global Causes of Deaths in 2000 (World Health Organization

(WHO), 2018)

2000 2010 2016

The Top 10 Causes of Death from 2000 to 2016

Ischaemic Heart Disease Stroke

Lower Respiratory Infections Chronic Obstructive Pulmonary Disease

Diarrhoeal Diseases Tuberculosis

HIV/AIDS Preterm Birth Complications

Trachea, Bronchus, Lung Cancers Road Injury

Diabetes Mellitus Alzheimer Disease and Other Dementias

Linear (Diabetes Mellitus)

The diagnostic of diabetes mellitus is the first and fundamental stage of the disease. The disease

has its complications for long term periods. Significant organ damages are one of the side

effects of diabetes mellitus, and gradual development of significant organ damage threats the

human-being. The complications of diabetes mellitus are potential heart and blood vessel

diseases, neuropathy, kidney damage, eye damage, even blindness, the slow healing process,

hearing problems, sleep apnea, and Alzheimer's.

The life expectancy of people with diabetes mellitus is nearly seven years shorter than non-

diabetic people due to direct complications of diabetes mellitus. Also, people with diabetes

mellitus had 25-75% higher risk of dying from cancer infections, liver disease, lung disease,

mental disorders, intentional self-harm, external causes, and falls, independent of other risk

factors, such as age, gender, smoking, and weight (Murea, Ma, & Freedman, 2012).

Health Authorities define their public health policy according to the causes of death statistics.

For instance, the number of deaths in countries from heart disease and diabetes mellitus

increases in the last few years rapidly. The country has a keen interest in starting a vigorous

programme to encourage lifestyles to help prevent illnesses (World Health Organization

(WHO), 2018).

Moreover, some countries have developed policies for the prevention of diabetes mellitus and

the management of the disease. Some examples of international policies are in the specific

categories, such as financial incentives, health information and education, built environment,

inequalities in access, management and care.

The examples of financial incentives are that in 2009, the European Commission started a

program that provides young pupils with free fruits and vegetables, which successfully

increased students’ fruit and vegetable consumption (European Commission). In 2012, France

introduced a new tax policy, and they put a tax on sugar-sweetened drinks. Within four months,

sales of sugar-sweetened drinks had declined by 3.3% (Lavin & Timpson, 2013).

Health Information and Education Policies’ example is “Change4Life”, which is a social media-

based health campaign in the United Kingdom. The target group of the campaign is families

with children younger than 12 and it helps individuals to eat less and move more. Research

shows that Change4Life has contributed to increase the life expectancy in the United Kingdom

(Collins, et al., 2014). The Dutch recognition system for health promotion interventions is a

registration system that centrally gathers and evaluates the effectiveness of successful health

promotion interventions throughout the country (Brug, et al., 2010).

Furthermore, building a new policy has become essential for the prevention progress of diabetes

mellitus. In Germany, the National Cycling Plan introduced in 2002 with great success. The

plan focuses on capacity-building in urban centres to promote bicycling as transportation

(Germany Federal Ministry of Transport, Building and Urban Development, 2012). Also, this

project has proven successful in other countries, and in Beijing, biking trips rate increased by

3.2% (Sustainable Transport in China, 2013). In 2012, the city of Stockholm developed the

Urban mobility Strategy, with a focus on sustainable development of transport infrastructure,

including increasing the number of bike lanes and bicycle parking spots throughout the city

(The City of Stockholm, 2012). Stockholm is one of the highest-ranking cities in the world on

an independently developed scale of urban mobility and maturity (Little & UITP, 2014).

Besides, the United Kingdom and Australia started to apply inequalities in access. In the United

Kingdom, the older people in Manchester encouraged to use the Wii Fit Programme for being

active and to improve balance and coordination. The Outback Stores initiative in Australia

assists remote indigenous community stores with improving storage and decreasing costs of

perishable foods like fruits and vegetables (Outback Stores, 2014). Food subsidy programs have

shown to increase fruit and vegetable consumption (Black, et al., 2012).

Another International Policies’ category for the prevention of diabetes mellitus is the

management and care of the disease. The Danish General Practice Quality Unit published

improved adherence to guidelines for diabetes mellitus and uses data from diagnoses,

procedures, prescribed drugs and laboratory results in primary care to monitor the quality of

vary for physicians (Guldberg, Vedsted, Kristensen, & Lauritzen, 2011). A health network

offers patients educational sessions, dietary counselling, supervised weight loss and exercise

program (Nolte, C., & McKee, 2008).

3.1.1 History of Diabetes Mellitus

Diabetes Mellitus has a long-term history. Diabetes mellitus comes from the Greek and Latin

words, which are diabetes, which means to siphon or pass through, from Greek word and

mellitus, is meaning honey or sweet, in Latin. At the beginning of the discovery period, medical

practitioners have named the disease as ‘the flesh and limbs melting down into urine’ in Greece.

More than 3000 years ago, the first known symptoms of diabetes mellitus mentioned in Egypt,

and the physician documented a symptom, which is frequent urination. Also, Indian healers

used ants for understanding the urine’s sugar level, and if the urine had a higher sugar level, the

ants came to the urine. Movement of the ants described as the sign of diabetes mellitus. The

Indian healers called the condition of the patient as ‘madhumeha,’ which means honey urine.

The earliest reference of diabetes mellitus was during the third century before the common era

(B.C.E) by Apollonius of Memphis. Approximately all healers or medical practitioners noted

that the disease was more common in heavy, wealthy people of the population more than other

parts of the community. They believed that these wealthy people ate more than healthy people

and less active.

After discovering the disease, the search for the treatment has started. The Greek physicians

suggested that the treatment of the disease was an exercise on horseback, preferably, and they

believed that this would decrease the excessive urination needs. Joseph von Mering and Oscar

Minkowski noticed the importance of the pancreas for arranging blood sugar levels in 1889.

Mering and Minkowski’s experiment was to remove the pancreas from dogs. After that, dogs

developed diabetes mellitus and died shortly afterwards. The experiment showed the role of the

pancreas. In 1910, Sir Edward Albert Sharpey-Schafer introduced that the lack of a particular

chemical, which was produced by the pancreas, was the cause of diabetes mellitus. After 11

years, the new experiment with dogs developed by Frederick Banting and Charles Best. In this

experiment, the healthy dogs’ pancreatic islet cells removed and placed in the dogs with

diabetes mellitus. Doing this exchange displayed the insulin hormone. A year later from this

discovery, Leonard Thompson was the first person to get an injection of insulin to treat diabetes

mellitus, and he lived 13 years more after the injection and died because of pneumonia. The

research, which showed differences between type 1 diabetes mellitus and type 2 diabetes

mellitus, was published by Sir Harold Percival Himsworth in 1936. He was the first person who

mentioned insulin resistance. The first human-based insulin generated in 1978 and named

humilin, which has a similar human insulin structure. After this discovery, the first blood

glucose monitors (1980s), insulin pen delivery system (1986) and external insulin pumps

(1990s) invented. The modern treatment methodologies are to use animal-based insulin

injections and other oral medicines, such as metformin.

3.1.2 Statistics of Diabetes Mellitus

There is no country free of diabetes mellitus in the world, and the prevalence rate of diabetes

mellitus is enlarging day by day. The disease is becoming a chronic problem in the world. The

World Health Organization (WHO), the International Diabetes Federation (IDF) and other

researchers completed some projections about the prevalence rate of diabetes mellitus. The

estimations display that the enlargement of diabetes mellitus has an increasing trend.

3.1.2.1 Prevalence of Diabetes Mellitus

Prevalence defines as the number of persons in the given population affected by a disease at a

specific time divided by the number of persons in the given population at that time and is a

measure of the burden of disease in the population (Chamany & Tabaei, 2010). Many factors

affect the diabetes mellitus prevalence rate, especially the global obesity prevalence rate

changes. The International Diabetes Federation divided the world into seven different regions.

The regions are Africa, Eastern Mediterranean and Middle-East (EMME), Europe, North

America, South and Central America (SACA), South Asia, and Western Pacific for calculating

the regional prevalence of diabetes mellitus.

The highest regional prevalence for 2010 was for North America, followed by the Eastern

Mediterranean and Middle-East and South Asia. The African region is expected to have the

most massive proportional increase in adult diabetes mellitus numbers by 2030, followed by

Eastern Mediterranean and Middle-East, through North America will continue to have the

world’s highest prevalence (Shaw, Sicree, & Zimmet, 2010).

The developed countries and developing countries have differences in diabetes mellitus

prevalence. The developing countries prevalence is higher than the developed countries'

prevalence from 2010 to 2030. The prevalence rates for the developing countries and developed

countries are 69% and 20%, respectively, and the total population increase expectations will be

36% for the developing countries and 2% for the developed countries.

Increases in diabetes mellitus numbers are expected with a doubling for the over 60-year age-

group for the developing countries, while for developed countries, an increase only expects

amongst the over 60s, with slight decreases predicted for the younger age-groups (Shaw, Sicree,

& Zimmet, 2010). The projections for 2010 and 2030, the top 10 countries for diabetes mellitus

prevalence in 2010 and 2030 and the top 10 countries for numbers of people aged 20 and 79

years with diabetes mellitus in 2010 and 2030 are illustrated in Figure 3.2, Appendix A (Table

A-1) and Appendix A (Table A-2). The prevalence of diabetes mellitus and estimated diabetes

mellitus numbers among adults aged from 20 to 79 years for the years 2010 and 2030 for 80

most populous countries are shown in Appendix A (Table A-3) (Shaw, Sicree, & Zimmet,

2010).

Also, the International Diabetes Federation completed another estimation for 2013 and 2035,

and the number of expected diabetes mellitus patients is 592 million in 2035, while the people,

who had diabetes mellitus, was 382 million in 2013 (Guariguata L. , Whiting, Hambleton,

Linnenkamp, & Shaw, 2014) (Guariguata L. , Whiting, Hambleton, Linnenkamp, & Shaw,

2014). It projected that most of the people, who will suffer from diabetes mellitus, live on low

income and middle-income countries. Also, the research shows that the Middle East and North

African region has the highest prevalence, while the lowest prevalence is in the Africa region

(5.7%). Africa will have the most significant proportional increase in the numbers of adults

with diabetes mellitus by 2035, with an increase of 109%. The overall diabetes mellitus increase

will be 55% by 2035.

Moreover, the International Diabetes Federation (IDF) published other estimations in 2017 and

2018. The publishment in 2017 shows the projections for 2015 and 2040 (Ogurtsova, et al.,

2017), while the projections of 2017 and 2045 displays in the last publishment of the

International Diabetes Federation (Cho, et al., 2018). The estimation for 2015 is 415 million

diabetic people aged 20-79 years old, and the health expenditure is predicted around 673 billion

US dollars. The increase in diabetic people's estimation was 642 million, and the cost was 802

billion US dollars in the next 25 years. The estimations are in Appendix A (Table A-4). Also,

it is estimated that in 2017, there are 451 million (aged 18-99 years) people with diabetes

mellitus worldwide, and the figures are expected to increase to 693 million by 2045 (Cho, et

al., 2018). The total healthcare expenditure will be 850 billion US dollars in 2017. All estimated

numbers for people, who will suffer from diabetes mellitus, are illustrated in Figure 3.3.

According to the World Health Organization and the International Diabetes Federation, 592

million people will suffer from diabetes mellitus in 2035, and it is estimated that 700 million

adults will have diabetes mellitus in 2045 (International Diabetes Federation, 2019).

Figure 3.2: The Projections for 2010 and 2030

Figure 3.3: The Estimated Numbers for People with Diabetes Mellitus (Cho, et al., 2018)

100150200250300350400450500

2010 2030

Number of Diabetes Mellitus Patients Projections

1980 2000 2003 2006 2009 2011 2013 2015

Projections for People with Diabetes Mellitus

3.1.2.2 Incidence of Diabetes Mellitus

Incidence is the new case numbers of the disease that occur in a given population over a specific

period and represents the rate at which a population develops the disease (Chamany & Tabaei,

2010). The incidence rate of diabetes mellitus is variable according to the countries and

individual-based conditions such as age, ethnicity, gender, temporal variation, and aetiological

variation. Also, each type of diabetes mellitus has a different incidence rate.

The incidence of type 1 diabetes mellitus varies enormously (nearly 400-fold) between

countries such as Venezuela and China that have meagre incidence rates, and Finland and

Sardinia, where the incidence is 40.9 and 37.8/100,000, respectively (Forouhi & Wareham,

2010). The highest incidence rate is observed in Sardinia, Italy. Generally, European, and North

American countries have high or intermediate incidence rates, while African countries have

intermediate incidence rates. The observation about seasonal change incidence presents that the

tendency of autumn and winter months are the highest.

Types of Diabetes Mellitus

Diabetes Mellitus is a metabolic and chronic disease. The disease is characterized by “the

presence of hyperglycemia due to defective insulin secretion, defective insulin action or both”

(Goldenberg & Punthakee, 2013). The chronic hyperglycemia of diabetes mellitus is associated

with long-term damage, dysfunction, and failure of different organs, such as the eyes, kidneys,

nerves, heart, and blood vessels (American Diabetes Association, 2010). The degree of

hyperglycemia (if any) may change over time, depending on the extent of the underlying disease

process (American Diabetes Association, 2010). The degree of hyperglycemia and disease

process relations are shown in Figure 3.4.

The etiopathogenetic classification categories used for diabetes mellitus, and the primary

categories are type 1 diabetes mellitus, type 2 diabetes mellitus and gestational diabetes

mellitus. The understanding of differences between type 1 diabetes mellitus and type 2 diabetes

mellitus is essential because each type of diabetes mellitus has different strategies for its

treatment methodologies. The types of diabetes mellitus are summarized as following

(Goldenberg & Punthakee, 2013):

• Type 1 diabetes mellitus encompasses diabetes mellitus that is primarily a result of

pancreatic beta-cell destruction and is prone to ketoacidosis. This form includes

cases due to an autoimmune process and those for which the etiology of beta-cell

destruction is unknown.

• Type 2 diabetes mellitus may range from predominant insulin resistance with

relative insulin deficiency to a predominantly secretory defect with insulin

resistance.

• Gestational diabetes mellitus refers to glucose intolerance with onset or first

recognition during pregnancy.

• Other specific types include a wide variety of relatively uncommon conditions,

primarily specific genetically defined forms of diabetes mellitus or diabetes-

associated with other diseases or drug use.

The diabetes mellitus cannot be cured, but the prevention of complications due to diabetes

mellitus is possible. The prevention methodologies of diabetes mellitus are good nutrition, daily

physical exercise, smoking cessation, monitoring of blood glucose regularly, oral medications

and insulin treatment if necessary.

Normoglycemia Hyperglycemia

Normal Glucose

Regulation

Impaired Glucose

Tolerance Or

Impaired Fasting Glucose (Pre-Diabetes)

Diabetes Mellitus

Not Insulin

Requiring

Insulin Requiring

for Control

Insulin Requiring

for Survival

Type 1*

Type 2

Other Specific Types**

Gestational Diabetes**

Figure 3.4: Disorders of Glycemia: Etiologic Types and Stages (American Diabetes

Association, 2010)

*Even after presenting in ketoacidosis, these patients can briefly return to normoglycemia

without requiring continues therapy (i.e., “honeymoon” remission);

**In rare instances, patients in these categories (e.g., Vacor toxicity, type 1 diabetes mellitus

presenting in pregnancy) may require insulin for survival.

Stages

3.2.1 Prediabetes

Prediabetes is a stage, which patients have a high risk of developing diabetes mellitus. The term

refers to Impaired Fasting Glucose (IFG) and Impaired Glucose Tolerance (IGT) (Goldenberg

& Punthakee, 2013). Prediabetes is a condition of elevated blood glucose, including impaired

fasting glucose and impaired glucose tolerance, which often precedes the onset of type 2

diabetes mellitus (Watson, 2017). All patients, who suffer from prediabetes, do not have to

develop diabetes mellitus and disease complications in the future. If the patients with

prediabetes are not treated properly, there is a considerable chance to suffer from type 2 diabetes

mellitus within five years of onset. Type 2 diabetes mellitus development and prediabetes

progress can be prevented with changing the lifestyle and receiving a proper treatment. The

diagnostic criteria for prediabetes are different from diabetes mellitus, and the criteria are shown

in Table 3-1.

The risk factors of prediabetes are the same as diabetes mellitus risk factors. The risk factors

are being age 45 or over, overweight and having obesity, a sedentary lifestyle, family history

for type 2 diabetes mellitus, minority race and ethnicity, and having Insulin Resistance (IR)

related conditions. The development of Insulin Resistance and hyperglycemia depends on

additional factors, which are a sedentary lifestyle, high-risk ethnicity, and family history of type

2 diabetes mellitus, except obesity.

In children and teenagers, the primary risk factor is obesity for diabetes mellitus and type 2

diabetes mellitus. Hormonal changes in puberty accelerate the progression of prediabetes to

type 2 diabetes mellitus, and beta-cell failure and type 2 diabetes mellitus progression occur

more quickly in adolescents than adults (Watson, 2017).

Table 3-1: Diagnosis Criteria for Prediabetes (Watson, 2017)

Test Prediabetes Level

Lower Limit Upper Limit

Fasting Plasma Glucose (FPG) 100 mg/dL 125 mg/dL

Oral Glucose Tolerance Test (OGTT): 2-

Hour Post Glucose 140 mg/dL 199 mg/dL

Hemoglobin A1C (A1C)

5.7% 6.4%

The risk extends below the lower limit of

the range and is disproportionately greater at

the higher end of the range

3.2.2 Type 1 Diabetes Mellitus

Approximately 10% of total diabetes mellitus patients suffer from Type 1 Diabetes Mellitus

(T1DM). Type 1 Diabetes Mellitus can be diagnosed at any age, especially roughly 50% of

cases appear after 20 years old. The type 1 diabetes mellitus incidence rate is high in developed

countries, and it also warns the communities about increasing incidence for children under five

years old. Obesity is one of the primary reasons for the higher type 1 diabetes mellitus

incidence. The most significant increase in type 1 diabetes mellitus incidence is in non-

European ancestry groups are known to have overlapping but also distinct genetic associations

with type 1 diabetes mellitus (Robertson & Rich, 2018).

Type 1 diabetes mellitus, formerly known as insulin-dependent diabetes mellitus, is a chronic

disease characterized by hyperglycemia secondary to inadequate production of insulin by the

pancreas (Thrower & Bingley, 2010). There is a relationship between type 1 diabetes mellitus

and beta islet cells in the pancreas. Type 1 diabetes mellitus defines an organ-specific

autoimmune disease in which self-reactive T lymphocytes, activated by autoantigen, destroy

the pancreatic insulin-producing beta islet cells in the islets of Langerhans (Thrower & Bingley,

2010). Description of type 1 diabetes mellitus is onset in childhood or adolescence, lean body

habitus, acute onset of osmotic symptoms, ketosis-prone, high concentration of islet

autoantibodies and high prevalence of genetic susceptibility alleles.

Genetic factors and environmental factors have an impact on type 1 diabetes mellitus. Some

environmental factors are viral infections, toxins and chemical compounds, higher birth weight

and infant growth, beta-cell stress, dietary deficiencies, psychological stress. Over 50 loci in

human-being genetics have identified related to type 1 diabetes mellitus increasing risk.

3.2.3 Type 2 Diabetes Mellitus

Roughly 90% of diabetes mellitus patients suffer from Type 2 Diabetes Mellitus. Type 2

Diabetes Mellitus is the primary reason for microvascular and macrovascular complications.

Also, the disease creates psychological and physical distress to both patients and care takers,

and increases the burden on the health-care system. The reasons behind the increase of type 2

diabetes mellitus burden are rising obesity, sedentary lifestyle, and inadequate diets. Over 70

loci in human DNA are responsible for type 2 diabetes mellitus.

Type 2 Diabetes Mellitus (T2DM) does not have a specific definition like Type 1 Diabetes

Mellitus (T1DM). However, patients who do not fulfill the criteria of type 1 diabetes mellitus

consider having type 2 diabetes mellitus (Groop & Pociot, 2014). Type 2 Diabetes Mellitus

develops when pancreatic beta-cells no longer can increase their insulin secretion to compensate

for increasing insulin resistance imposed by increasing obesity (Lyssenko, et al., 2008).

Certain ethnic popularities have a higher risk of type 2 diabetes mellitus. The high-risk countries

for developing type 2 diabetes mellitus are in Asia, the Middle East and America. The

development of the lifetime risk type 2 diabetes mellitus is 40% for individuals who have one

parent with type 2 diabetes mellitus and almost 70% if both parents are affected (Groop &

Pociot, 2014). Also, if the mother has type 2 diabetes mellitus, the risk of suffering from

diabetes mellitus is higher than having the father with type 2 diabetes mellitus. Risk factors for

type 2 diabetes mellitus are illustrated in Table 3.2.

Environmental risk factors for type 2 diabetes mellitus are pollution, chemical contaminants in

food or water, stress, and health supplements. Type 2 diabetes mellitus is affected by two

different types of pollution: land pollution and air pollution. The studies show that land

pollutants, such as pesticides, and herbicides) upset glucose metabolism in the human body and

influence insulin resistance level. Also, exposure to traffic-related pollutants (particulate matter

(PM) and nitrogen dioxide (NO2)) is associated with type 2 diabetes mellitus higher incidence

rates in a dose-dependent manner (Murea, Ma, & Freedman, 2012). The mortality rate and

diagnosis from type 2 diabetes mellitus increase with food chemical contaminants (dioxins and

polychlorinated biphenyls (PCBs)) or water (arsenic) and occupational exposures to various

toxins (Murea, Ma, & Freedman, 2012). There is a strong relationship between stress factors

and insulin resistance. Environmental factors impact and possible conceptual framework on

type 2 diabetes mellitus are shown in Figure 3.5. The socio-ecological theories in the framework

emphasize human behaviour is influenced by their ability, and when their socio-demographic,

psychosocial, economic, organizational and physical environment are supportive (Dendup,

Feng, Clingan, & Astell-Burt, 2018).

There is a strong relationship between patients’ age and the challenges of type 2 diabetes

mellitus. While the patient is younger than 25 years old, the management of diabetes mellitus

is more challenging than older adults. Younger patients have a higher risk of developing some

complications. Young-Onset type 2 diabetes mellitus is an aggressive phenotype, has classified

in patients aged between 15 and 30 years old. Young-onset type 2 diabetes mellitus increases

the risk of cardiovascular death, macrovascular complications, and neuropathy scores. Also,

intensive management of type 2 diabetes mellitus in elderly patients (65 years of age or older)

must balance against the management of other comorbidities, cognitive impairment, and

hypoglycemia risk (Chatterjee, Khunti, & Davies, 2017).

Furthermore, type 2 diabetes mellitus is characterized by increased hyperinsulinemia, insulin

resistance, and pancreatic beta-cell failure, with up to %50 cell loss at diagnosis (Chatterjee,

Khunti, & Davies, 2017). Young patients (aged 10-17 years) may lose more beta-cell, and it

shows the importance of earlier diagnosis and earlier treatment of the patients. Also, type 2

diabetes mellitus development is affected by some organ involvement, such as the pancreas

(beta-cells and alpha-cells), liver, skeletal muscle, kidneys, brain, small intestine, and adipose

tissue.

Early detection of type 2 diabetes mellitus helps to the optimization of qualified outcomes

through screening and intensive patient-centred management. Education, self-management

programmes and psychological support are essential elements for better disease management.

The optimization of existing strategies for treating type 2 diabetes mellitus is displayed in

Figure 3.6.

Glycaemic control management and minimization of type 2 diabetes mellitus complications are

possible with the early diagnosis of the disease. The lifestyle changes help to reach optimal

results of the type 2 diabetes mellitus treatment process. The changes are weight management,

increased physical activity, healthy diet, smoking cessation, moderation of alcohol

consumption, and glucose-lowering therapies to reach individualized glycaemic targets.

Considerable evidence suggests that the prevention of type 2 diabetes mellitus is possible

(Chatterjee, Khunti, & Davies, 2017). The prevention strategies include managing obesity,

impaired glucose regulation with diet and exercise interventions, to a lesser extent,

pharmacological therapy, such as metformin.

Figure 3.5: Environment Impacts Possible Pathways for Type 2 Diabetes Mellitus (Dendup,

Feng, Clingan, & Astell-Burt, 2018)

Environmental

Determinants

Risk Factors/

Exposures

Intermediary

Outcomes

Primary

Outcome

Health Services

Physical Activity

Resources

Safety/ Violence

Amenities

Walkability

Urban Sprawl

Area Condition

Public Transport

Green Space

Physical

Inactivity

Unhealthy Diet

/ Behaviours

Stress

Disturbed

Social

Isolation/ Fear

Air Pollution

Noise Pollution

Traffic

Obesity

Hypertension

Prediabetes

Blood Lipid

Levels

Type 2

Diabetes

Mellitus

Figure 3.6: Optimization of Existing Strategies for Treating Type 2 Diabetes Mellitus

(Chatterjee, Khunti, & Davies, 2017)

Personalised Medicine Disease-Modifying Treatments That

Improve Disease Progression

Individualised

Targeted

Targets

Improved

Combination

Convenience in The

Delivery of Insulin

Early Diagnosis and

Treatment

Ongoing Trials –

Greater Understanding

of Long-Term

Treatments

Optimal Resource Use

Simplified Treatment

Regiments

Extending Use of

Treatments into New

Patients Group

Table 3-2: Risk Factors for Type 2 Diabetes Mellitus (Chamany & Tabaei, 2010)

Age ≥45 years

Obesity (Body-Mass Index ≥ 25 𝑘𝑘𝑘𝑘 𝑚𝑚2⁄ )

Family History of Diabetes Mellitus (i.e., parents or siblings with diabetes mellitus)

Habitual Physical Inactivity

Race/ Ethnicity (i.e., Africa-Americans, Hispanic Americans, Native Americans, Asian

Americans, and Pacific Islanders)

Previously Identified Impaired Fasting Glucose or Impaired Glucose Tolerance

History of Gestational Diabetes Mellitus or Delivery of a Baby Weighing > 9 lb

Hypertension (≥140/90 mmHg in adults)

HDL Cholesterol ≤35 mg/dl (0.9 mmol/l) and/or A Triglyceride Level of ≥ 250 mg/dl (2.82

mmol/l)

Polycystic Ovarian Syndrome

History of Vascular Disease

3.2.4 Gestational Diabetes Mellitus (GDM)

If the pregnant woman has glucose intolerance during the pregnancy, the situation refers to

gestational diabetes mellitus (GDM). The high-risk factors for GDM are older age, marked

obesity, personal GDM history, glycosuria, or strong family history. The low-risk groups'

characteristics are the following (American Diabetes Association (ADA), 2004):

• Age is lower than 25 years old,

• Weight normal before pregnancy,

• Member of an ethnic group with a low prevalence of gestational diabetes mellitus,

• No known diabetes mellitus in first-degree relatives,

• No history of abnormal glucose tolerance,

• No history of the poor obstetric outcome.

After delivery, a considerable majority of women with GDM return to the average glucose

tolerance level. However, 47% of these women go on to develop type 2 diabetes mellitus within

five years, although a systematic review found this range to be from 3 to 70% depending on the

population (Chamany & Tabaei, 2010).

The complications for baby may be excessive birth weight, early (preterm) birth and respiratory

distress syndrome, low blood sugar (hypoglycemia) and type 2 diabetes mellitus risk later in

life. Also, the risk factors of the mother are high blood pressure and preeclampsia and future

type 2 diabetes mellitus.

The treatment methodologies of GDM are whether insulin or diet modification and the

prevention methodologies of GDM are healthy diets, being physically active, and losing weight

before pregnancy.

Symptoms of Diabetes Mellitus

The symptoms vary according to types of diabetes mellitus, but there are some common

symptoms for all types of diabetes mellitus. The symptoms are the following:

• Excessive and increased thirst,

• Frequent and increased urination,

• Unexplained weight loss or gain,

• Extreme or increased hunger,

• Extreme fatigue, lack of energy, or feeling very tired,

• Tingling or numbness in the hands or feet,

• Sudden vision changes, or blurry vision,

• Frequent or recurring or increased infections (yeast infections, skin infections, vaginal

infections, urinary tract infections),

• Slow-healing wounds, cuts, or sores,

• Skin problems (patches of dark skin, dehydrated skin, itching skin),

• Presence of ketones in the urine,

• Irritability or mood changes,

• Trouble getting or maintaining an erection (Erectile Dysfunction (ED)),

• Decreased sex drive,

• Poor muscle strength

• Nausea, vomiting,

• Dry mouth.

Type 1 diabetes mellitus symptoms can start more quickly than symptoms of type 2 diabetes

mellitus. However, type 2 diabetes mellitus may not be noticed until the complications show

up, and most of the patients do not have symptoms of diabetes mellitus. Gestational diabetes

mellitus does not have any symptoms. The regular blood glucose level test helps to diagnose

gestational diabetes mellitus between the 24th week of pregnancy and the 28th week of

pregnancy. Some women will experience excessive thirst or urination as a symptom.

Diagnosis Methodologies of Diabetes Mellitus

The diagnosis of diabetes mellitus can be possible with measuring blood glucose levels. There

are four different kinds of methodologies for measuring blood glucose levels. The

methodologies are a fasting plasma glucose (FPG), a 2-hour plasma glucose (2hPG), a 75-gram

oral glucose tolerance test (OGTT) and a glycated hemoglobin (A1C). The advantages and

disadvantages of diagnostic tests for diabetes mellitus are shown in Appendix A (Table A-5),

and the diagnosis criteria of diabetes mellitus are illustrated in Appendix A (Table A-6).

3.4.1 Fasting Plasma Glucose (FPG)

The lowest-cost and most known screening test for prediabetes and diabetes mellitus is the

Fasting Plasma Glucose (FPG). It is more sensitive than A1C for diagnosing prediabetes and

diabetes mellitus but less sensitive than 2-Hour Plasma Glucose (2-h PG) (Watson, 2017). Short

term factors (such as stress and illnesses) affect the test. After fasting or not eating anything at

least 8 hours, blood glucose levels can be measured in Fasting Plasma Glucose Test.

3.4.2 2-Hour Plasma Glucose (2-h PG)

2-Hour Plasma Glucose test refers to the Oral Glucose Tolerance Test. The test is more sensitive

than Fasting Plasma Glucose (FPG) and Hemoglobin A1C (A1C), diagnoses more cases of

diabetes mellitus than the other methods and is an early marker for Impaired Glucose Tolerance

(IGT) and prediabetes (Watson, 2017). The test is preferable for children, but it is less cheap.

3.4.3 Hemoglobin A1C

Hemoglobin A1C is not affected by short-term factors, and the reflection of long-term glycemic

load can be measured with the test. Also, the test does not need any further preparations, either

by patients or medical practitioners. A1C is less sensitive and detects fewer prediabetes or

diabetes mellitus cases than of the tests that measure blood glucose (Watson, 2017). However,

the test is affected by conditions related to red blood cell turnover rate (i.e. genetic variances in

people, certain hemoglobinopathies, liver or kidney disease, heavy bleeding or blood

transfusion, iron deficiency anemia).

3.4.4 Diagnostic Criteria

The patients’ conditions and test results decide the frequency of screening tests. The test should

be repeated at least every three years, even though the current test result is negative and the

patient shows no multiple risk factors. However, if the patient has multiple risk factors, the test

should be repeated more often. The confirmatory test becomes obligatory if positive screening

test results and unclear hyperglycemia symptoms exist. The confirmatory test is to repeat the

same test or requesting another screening test to be sure about the patient’s diagnostic result on

a subsequent day. The American Diabetes Association recommends repeating the same test for

confirmation, but positive results from 2 different tests also confirm the diagnosis (Watson,

2017). If both test results are not affirmative for the diagnosis of diabetes mellitus, the repetition

of the test period should be from 3 months to 6 months. If the diagnosis of prediabetes is exact,

the treatment process has to start without repeating the confirmation test, and the risk of

development of type 2 diabetes mellitus is high. The prediabetes process in patients should be

monitored at least annually.

Challenges of Diagnosis Diabetes Mellitus

One of the main challenges of diabetes mellitus is repeating the diagnosis tests more than once.

The repeating process increases the cost and time of the treatment process. The repeating

process starts after a single laboratory test. In the absence of symptomatic hyperglycemia, if a

single laboratory test result is in the diabetes mellitus range, a repeat confirmation laboratory

test (FPG, A1C, 2hPG in a 75 g OGTT) must be done on another day (Goldenberg & Punthakee,

2013). The same test should be repeated (in a timely fashion) for confirmation. A random

Plasma Glucose (PG) in the diabetes mellitus range in an asymptomatic individual should be

confirmed with an alternate test (Goldenberg & Punthakee, 2013). If there is a chance to have

type 1 diabetes mellitus, the confirmation test should be done as soon as possible, and the

treatment has to start. The benefit of early treatment will prevent the deterioration process of

insulin. If the two diagnostic test results show that the blood glucose level for diabetes mellitus,

the diagnosis of the disease is positive. When the results are available and discordant, the test

whose result is above the diagnostic level should be repeated and the diagnosis should be made

based on the repeat test (Goldenberg & Punthakee, 2013).

4. FUZZY SET THEORY

Introduction of Fuzzy Set Theory

The answers to real-life problems cannot be absolute and clear forever. The questions’ answers

sometimes can be different from an absolute “yes” or “no”. Some questions are related to many

factors, such as personal choices or environmental factors. For example, the question is, “is it

cold today?”. The actual temperature of the day is 5 Celsius, and there is no wind. People can

answer like “Yes, it is cold today.” or “No, it is not cold today.” or “It is a little cold today”.

Different people have different opinions about temperature. Therefore, answering this kind of

question needs more than the usage of bivalent logic, which defines a theory has one correct

answer, either “yes” or “no”.

A Classical Set, which has crisp boundaries, includes the bivalent logic situations. The

reflection of human expertise is not possible with the usage of the classical set. For example, a

classical set determines as real numbers are smaller than 8. The boundary of the classical set is

until 8. If we want to compare a number, which is 8.001; the classical set cannot cover the

number, because of the boundary. In this kind of situation, the Fuzzy Set theory is more useful

against the classical set. The Fuzzy Set, as the name implies, is a set without any boundaries.

The transition between “belonging to a set” and “not belonging to a set” is gradual, and the

smooth transition is described by membership functions, which reflect fuzzy sets flexibility in

linguistic expressions modelling (Jang, Sun, & Mizutani, 1997).

Nowadays, the popularity of intelligent system usage is increasing, and the field of intelligent

system usage is expanding. One of the most popular fields in intelligent systems is to solve

medical problems. Creating an intelligent system using Fuzzy Logic theory can help to diagnose

diabetes mellitus. Therefore, the chapter will discuss the fundamental concepts, functions, and

characteristics of the Fuzzy Set theory and Fuzzy Logic.

Fuzzy Set Theory

4.2.1 Definition

Dr. Lotfi Zadeh, who was a professor in the Computer Science department at the University of

California in Berkeley, introduced Fuzzy Sets and Fuzzy Logic (Zadeh, Fuzzy Sets, 1965). Dr.

Zadeh proposed a methodology, which can deal with uncertainty and vagueness in a

mathematical framework. Although the Fuzzy Set theory is simple, the theory, which can deal

with uncertainty and vagueness easily, is a potent, effective, and efficient tool. The summary

of the fuzzy set properties is a multivalued logic defined boundary, an element can partly belong

to a fuzzy set, and an element is categorized by degree.

The definition of the fuzzy set ‘A’ in ‘X’ is a set of ordered pairs (Zadeh, Fuzzy Sets, 1965):

𝐴𝐴 = ��𝑥𝑥,𝜇𝜇𝐴𝐴(𝑥𝑥)��𝑥𝑥 ∈ 𝑋𝑋� , Equation 4.1

Where membership function (MF) of the fuzzy set is 𝜇𝜇𝐴𝐴 , which is between 0 and 1. Generally,

X is called the universe of discourse or universe. The universe can be in a discrete space or

continuous space. The examples of the universe of discourse are a sensible number of children

in a family for discrete space, and ages of human for continuous space. The fuzzy set

construction relies on two elements: a suitable universe of discourse identification and an

appropriate membership function specification.

4.2.2 Basic Operations on Fuzzy Sets

The basic operations on the standard sets are union, intersection and complement. The basic

identities of standard sets are shown in Appendix B (Table B-1). Also, the fuzzy sets have the

same operations as the standard sets. Zadeh’s seminal paper has defined the operations of the

fuzzy sets (Zadeh, Fuzzy Sets, 1965). The definitions of the basic operations on fuzzy sets are

shown below.

The containment or subset means that fuzzy set ‘B’ is a subset of fuzzy set ‘A’ (or, ‘B’ is

contained ‘A’) if and only if for all 𝑥𝑥, 𝜇𝜇𝐵𝐵(𝑥𝑥) ≤ 𝜇𝜇𝐴𝐴(𝑥𝑥). The mathematical symbols of the

containment are (Zadeh, Fuzzy Sets, 1965);

𝐵𝐵 ⊆ 𝐴𝐴 ⟺ 𝜇𝜇𝐵𝐵(𝑥𝑥) ≤ 𝜇𝜇𝐴𝐴(𝑥𝑥), Equation 4.2

One of the basic operations is union or disjunction. The two fuzzy sets’, ‘A’ and ‘B’, union

creates a fuzzy set, ‘C’. It can be written as 𝐶𝐶 = 𝐴𝐴 ∪ 𝐵𝐵 or 𝐶𝐶 = 𝐴𝐴 𝑂𝑂𝑂𝑂 𝐵𝐵. The definition of the

union can be summarized as the “smallest” fuzzy set, which contains both a fuzzy set ‘A’ and

a fuzzy set ‘B’ (Zadeh, Fuzzy Sets, 1965). Also, if any fuzzy set comprises of both fuzzy sets,

‘A’ and ‘B’, the fuzzy set will contain 𝐴𝐴 ∪ 𝐵𝐵.

𝜇𝜇𝐶𝐶(𝑥𝑥) = max(𝜇𝜇𝐴𝐴(𝑥𝑥),𝜇𝜇𝐵𝐵(𝑥𝑥)) = 𝜇𝜇𝐴𝐴(𝑥𝑥) ∨ 𝜇𝜇𝐵𝐵(𝑥𝑥) , Equation 4.3

Furthermore, the intersection is another basic operation in fuzzy sets. The intersection of two

fuzzy sets, ‘A’ and ‘B’, is the ‘largest’ fuzzy set containing both fuzzy sets. ‘C’ is the

intersection of two fuzzy sets, ‘A’ and ‘B’, and the intersection can be shown as 𝐶𝐶 = 𝐴𝐴 ∩ 𝐵𝐵 or

𝐶𝐶 = 𝐴𝐴 𝐴𝐴𝐴𝐴𝐴𝐴 𝐵𝐵 (Zadeh, Fuzzy Sets, 1965).

𝜇𝜇𝐶𝐶(𝑥𝑥) = min( 𝜇𝜇𝐴𝐴(𝑥𝑥),𝜇𝜇𝐵𝐵(𝑥𝑥)) = 𝜇𝜇𝐴𝐴(𝑥𝑥) ∧ 𝜇𝜇𝐵𝐵(𝑥𝑥) , Equation 4.4

The fuzzy sets complement operator should meet three requirements, which are illustrated in

Equation 4.5 (Jang, Sun, & Mizutani, 1997). The operator is a continuous function, which

defines 𝐴𝐴: [0,1] → [0,1].

𝐴𝐴 (0) = 1 𝑎𝑎𝑎𝑎𝑎𝑎 𝐴𝐴(1) = 0 (𝑏𝑏𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎𝑎𝑎𝑏𝑏𝑏𝑏)

Equation 4.5 𝐴𝐴(𝑎𝑎) ≥ 𝐴𝐴(𝑏𝑏) 𝑖𝑖𝑖𝑖 𝑎𝑎 ≤ 𝑏𝑏 (𝑚𝑚𝑏𝑏𝑎𝑎𝑏𝑏𝑚𝑚𝑏𝑏𝑎𝑎𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑏𝑏)

𝐴𝐴�𝐴𝐴(𝑎𝑎)� = 𝑎𝑎 (𝑖𝑖𝑎𝑎𝑖𝑖𝑏𝑏𝑖𝑖𝑏𝑏𝑚𝑚𝑖𝑖𝑏𝑏𝑎𝑎)

The two fuzzy sets’, ‘A’ and ‘B’, the intersection is determined on general by a function

𝑇𝑇: [0,1] × [0,1] → [0,1]. The intersection is shown in Equation 4.6 (Zadeh, Fuzzy Sets, 1965).

𝜇𝜇𝐴𝐴 ∩𝐵𝐵(𝑥𝑥) = 𝑇𝑇�𝜇𝜇𝐴𝐴(𝑥𝑥),𝜇𝜇𝐵𝐵(𝑥𝑥)� = 𝜇𝜇𝐴𝐴(𝑥𝑥) ∗� 𝜇𝜇𝐵𝐵(𝑥𝑥) , Equation 4.6

Generally, the fuzzy intersection operators usually refer to T-norm or triangular norm operators,

which are illustrated in Appendix B (page 138) (Dubois & Prade, 1980).

Moreover, the determination of two fuzzy sets’, ‘A’ and ‘B’, the union is created generally by

a function 𝑆𝑆: [0,1] × [0,1] → [0,1]. Two membership grades of the intersection are in Equation

4.7 (Jang, Sun, & Mizutani, 1997). The class of fuzzy set class operators is called as T-conorm

or S-norm operators, which is in Appendix B (page 139).

𝜇𝜇𝐴𝐴 ∪𝐵𝐵(𝑥𝑥) = 𝑆𝑆�𝜇𝜇𝐴𝐴(𝑥𝑥),𝜇𝜇𝐵𝐵(𝑥𝑥)� = 𝜇𝜇𝐴𝐴(𝑥𝑥) +� 𝜇𝜇𝐵𝐵(𝑥𝑥) , Equation 4.7

4.2.3 Membership Functions

The characterization of fuzzy sets depends on fuzzy sets’ membership functions. There are two

different types of membership functions categories: one dimension and two dimensions. The

differences of types are inputs and parameters of membership functions.

4.2.3.1 One Dimension Membership Functions

Membership Functions of one dimension is that membership functions with a single input.

Triangular membership functions, Trapezoidal membership functions, Gaussian membership

functions, Generalized Bell membership functions, and Sigmoidal membership functions are

examples of the category. The triangular membership functions and trapezoidal membership

functions are preferable for real life and other problems. The reason behind the situation is the

simplicity and efficiency of the membership functions. The smoothness and concise notation

of the membership functions have the effect of increasing popularity. The definition and

formulation of membership functions are explained below (Jang, Sun, & Mizutani, 1997). The

illustration of membership functions is in Appendix B (Figure B-1), and the explanation of

membership functions except Triangular Membership Functions is in Appendix B (page 139-

4.2.3.1.1 Triangular Membership Functions

A triangular membership function has three parameters (a, b, and c). The parameters show the

three corner coordinates of the x-axis for the triangular membership function, and the

parameters should align as 𝑎𝑎 < 𝑏𝑏 < 𝑚𝑚. The mathematical formulation of the membership

function and alternative expression by using min and max operators are shown in Equation 4.8

and Equation 4.9, respectively (Jang, Sun, & Mizutani, 1997).

𝑚𝑚𝑏𝑏𝑖𝑖𝑎𝑎𝑎𝑎𝑘𝑘𝑖𝑖𝑡𝑡 (𝑥𝑥;𝑎𝑎, 𝑏𝑏, 𝑚𝑚) =

⎩⎪⎨

⎪⎧

0, 𝑥𝑥 ≤ 𝑎𝑎𝑥𝑥 − 𝑎𝑎𝑏𝑏 − 𝑎𝑎

, 𝑎𝑎 ≤ 𝑥𝑥 ≤ 𝑏𝑏𝑚𝑚 − 𝑥𝑥𝑚𝑚 − 𝑏𝑏

, 𝑏𝑏 ≤ 𝑥𝑥 ≤ 𝑚𝑚

0, 𝑚𝑚 ≤ 𝑥𝑥

Equation 4.8

𝑚𝑚𝑏𝑏𝑖𝑖𝑎𝑎𝑎𝑎𝑘𝑘𝑖𝑖𝑡𝑡 (𝑥𝑥; 𝑎𝑎, 𝑏𝑏, 𝑚𝑚) = max �min �𝑥𝑥−𝑎𝑎𝑏𝑏−𝑎𝑎

, 𝑐𝑐−𝑥𝑥𝑐𝑐−𝑏𝑏

� , 0� , Equation 4.9

4.2.3.2 Two Dimensions Membership Functions

The membership functions of two dimensions are a membership function with two inputs, and

each input is in a different universe of discourse. The example of membership functions of two

dimensions is the cylindrical extensions of one-dimensional fuzzy sets.

4.2.4 Fuzzy Relations

The combination of fuzzy relations and composition operators is possible in different product

spaces. Although the max-min composition (Zadeh, Fuzzy Sets, 1965) is the best composition

operator, different composition operators exist in the literature. The explanations of max-min

composition and max-product composition are displayed below.

If the 𝑂𝑂 is defined on 𝑋𝑋 × 𝑌𝑌 and 𝑆𝑆 is defined on 𝑌𝑌 × 𝑍𝑍, and 𝑂𝑂 and 𝑆𝑆 are the two fuzzy relations,

the max-min composition of the fuzzy relations is shown in Equation 4.10 (Kundu, 1998) and

short version of the composition is illustrated in Equation 4.11 (Kundu, 1998). The max-min

composition is also known as the max-min product.

𝑂𝑂 ∘ 𝑆𝑆 = �𝑚𝑚𝑎𝑎𝑥𝑥𝑦𝑦𝑚𝑚𝑖𝑖𝑎𝑎(𝜇𝜇𝑅𝑅(𝑥𝑥, 𝑏𝑏),𝜇𝜇𝑆𝑆(𝑏𝑏, 𝑧𝑧) )�𝑥𝑥 ∈ 𝑋𝑋, 𝑏𝑏 ∈ 𝑌𝑌, 𝑧𝑧 ∈ 𝑍𝑍�, Equation 4.10

𝜇𝜇𝑅𝑅∘𝑆𝑆(𝑥𝑥, 𝑧𝑧) = 𝑚𝑚𝑎𝑎𝑥𝑥𝑦𝑦min [𝜇𝜇𝑅𝑅(𝑥𝑥,𝑏𝑏), 𝜇𝜇𝑆𝑆(𝑏𝑏, 𝑧𝑧)],

𝜇𝜇𝑅𝑅∘𝑆𝑆(𝑥𝑥, 𝑧𝑧) = ∨𝑦𝑦 [𝜇𝜇𝑅𝑅(𝑥𝑥, 𝑏𝑏) ∧ 𝜇𝜇𝑆𝑆(𝑏𝑏, 𝑧𝑧) ] Equation 4.11

Moreover, another composition operation is the max-product composition. Due to the most

laborious mathematical analysis, the max-product composition is developed as an alternative

methodology. The max-product composition helps to reach at a better mathematical tractability.

The formulation of max-product composition is shown in Equation 4.12 (Markovski, 2004).

𝜇𝜇𝑅𝑅∘𝑆𝑆(𝑥𝑥, 𝑧𝑧) = 𝑚𝑚𝑎𝑎𝑥𝑥𝑦𝑦[𝜇𝜇𝑅𝑅(𝑥𝑥,𝑏𝑏)𝜇𝜇𝑆𝑆(𝑏𝑏, 𝑧𝑧)], Equation 4.12

4.2.5 Linguistic Variables

Humanistic systems are one of the most significant factors for system analysis. Humanistic

systems are the behaviours, which deal with human judgement, perception, and emotions.

Zadeh (Zadeh, Quantitative Fuzzy Semantics, 1971) proposed linguistic variables as a concept,

which can efficiently deal with human expertise. The linguistic variables comprise the variable

(x), the term set (T(x)), the universe of discourse (X), syntactic rule (G) and semantic rule (M).

The example of the linguistic variable and the membership functions are shown in Appendix B

(Figure B-2). The typical membership functions for linguistic variables are shown in the figure.

When the variable is age, the term set comprises of very young, young, of, old, very old, the

universe of discourse is between 0 and 100 years old, the syntactic rule is the way of linguistic

variables illustration, and membership functions of each linguistic variable present the semantic

4.2.6 Fuzzy Conditional Statements

Fuzzy Conditional Statements are also known as Fuzzy Rules, Fuzzy Implication, or Fuzzy if-

then rules. The fuzzy conditional statements create a relationship with the linguistic variables.

The form of the fuzzy conditional statements expresses “if a is A then b is B”, where the

universe of discourses are X and Y, and the variables are a and b, respectively. The example

can be explained that when ‘a’ and ‘b’ are the linguistic variables and ‘A’ and ‘B’ are the values

of linguistic variables, respectively. Some examples of the fuzzy conditional statements are

below.

• If the temperature is 30 ℃, then the weather is hot.

• If the carbon dioxide (CO2) level is 400 parts per million, then the climate change

affection is high.

• If it is snowing, then the road is slippery.

• If a tomato is not red, then it is not ripe.

The variables before then of the Fuzzy Conditional Statements is the antecedent or premise,

and the variables after then in the fuzzy conditional statements is called the consequence or

conclusion.

4.2.7 Fuzzy Reasoning

Fuzzy reasoning or approximate reasoning is a procedure, which concludes from fuzzy

conditional statements’ set and known facts. Compositional rule inference (Zadeh, Outline of

A New Approach to the Analysis of Complex Systems and Decision Processes, 1973) is one of

the critical factors for fuzzy reasoning.

The modus ponens is the traditional two-valued logic in the basic rule of inference. The fuzzy

reasoning concept’s assumption is when the premise 1 (fact) is “x is 𝐴𝐴′” and the premise 2 (rule)

is “if x is A then y is B”, the consequence (conclusion) will be “y is 𝐵𝐵′”. When the fuzzy set

appropriate universes are A, 𝐴𝐴′, B and 𝐵𝐵′, the inference process is called approximate reasoning

or fuzzy reasoning or generalized modus ponens (if there is a particular case for modus ponens).

It is important to note that the fuzzy conditional statements do not have to be simple, always as

a single rule with a single antecedent. The fuzzy conditional statements can be in the general

form as a single rule with multiple antecedents and, also, multiple rules with multiple

antecedents (Jang, Sun, & Mizutani, 1997).

The fuzzy reasoning is divided into four steps: degrees of compatibility, firing strength,

qualified (induced) consequent membership functions and overall output membership function

(Jang, Sun, & Mizutani, 1997). Degrees of compatibility is a comparison between the known

facts and the antecedents of fuzzy rules for finding the degrees of compatibility. Also, the

combination of the degrees of compatibility and the antecedent membership functions in a rule

using fuzzy AND or OR operators are called the firing strength. The creation of a qualified

consequent membership function depends on the firing strength application based on the

consequent membership function of a rule. The overall output membership function is collected

all qualified consequent membership functions for getting an overall output membership

function.

Fuzzy Logic System

In the modern world, Fuzzy Logic is one of the most popular concepts for many fields, such as

decision analysis, automatic control, data classification, and medical applications. The reason

behind the extensive usage of Fuzzy Logic is to deal with uncertainty and vagueness by using

the Fuzzy Set theory as its foundation. The Fuzzy Logic introduced by Zadeh (Zadeh, Fuzzy

Logic, 1988).

The Fuzzy Logic creates the relationship between the system’s inputs and outputs under many

assumptions and approximations. The Fuzzy Logic system is designed for easy understanding,

flexible, and smoothly dealing with uncertain data. The Fuzzy Logic uses natural languages, so

the system helps experts to transform experts’ knowledge into the knowledge base practically.

The primary aim of the Fuzzy Logic system is to provide convenient fuzzy conditional

statements, linguistic variables, and membership functions, which are changeable according to

particular cases. Also, the Fuzzy Logic systems are known as a Fuzzy Inference System, Fuzzy

Rule-Based System, Fuzzy Expert System, Fuzzy Associative Memory, and Fuzzy Logic

Controller.

4.3.1 Fuzzy Inference System

A Fuzzy Inference System is a framework, which is based on the fuzzy set theory, Fuzzy

Conditional Statements, and Fuzzy Reasoning. The basic structure of the Fuzzy Inference

System comprises a rule base, a database (dictionary) and a reasoning mechanism (Jang, Sun,

& Mizutani, 1997). The rule base has the collection of fuzzy conditional statements, and the

membership functions in the fuzzy conditional statements are defined in the database. Also, the

fuzzy reasoning is to perform the inference procedure about the fuzzy conditional statements

and the facts for the derivation of a consistent output or conclusion.

The Fuzzy Inference System uses a nonlinear mapping between inputs and outputs. The

mapping process consists of numbers of fuzzy conditional statements, and the antecedent’s part

refers to the inputs, while the consequent’s fuzzy conditional statements show the outputs.

Figure 4.1 displays a general concept of the Fuzzy Inference System. The diagram explains how

the Fuzzy Inference System works for crisp or fuzzy inputs and outputs. The diagram steps are

explained below.

1. The system takes the crisp or fuzzy inputs. If the inputs are crisp, the inputs are

converted to the fuzzy sets, and the fuzzy sets are associated with the membership

functions’ degree. The first step refers to the fuzzification process.

2. The interaction between the inference engine and a knowledge base occurs in the

second step. The inference engine uses the fuzzified inputs for the determination of the

applicable fuzzy conditional statements, and the collection of the fuzzy conditional

statements and facts is kept in the knowledge base. There are more than one Fuzzy

Conditional Statements in the inference engine. Then, the production of one fuzzy

output distribution or fuzzy conclusion, in some cases, there can be more than one input,

based on fuzzy conditional statements and fuzzy reasoning is occurred in the inference

engine. The engine produces one fuzzy output distribution or fuzzy conclusion.

3. The third step is known as the defuzzification process. The defuzzification of the fuzzy

output is completed in the third step. After the defuzzification process, the fuzzy output

is converted to the crisp output. However, the defuzzification process happens if there

is a need for crisp value.

Two most used types of the Fuzzy Inference Systems exist in the literature: Mamdani Fuzzy

Inference System (Mamdani & Assilian, 1975), and Sugeno Fuzzy Inference System (Takagi

& Sugeno, 1985) (Sugeno & Kang, 1988) (Appendix B, page 142-143). The main Fuzzy

Inference Systems’ differences are the consequents of the fuzzy conditional statements,

aggregation and defuzzification processes (Jang, Sun, & Mizutani, 1997).

Figure 4.1: General Illustration of a Fuzzy Inference System

CRISP OR FUZZY INPUT

FUZZIFICATION

FUZZY INFERENCE ENGINE

DEFUZZIFICATION

CRISP OUTPUTS

4.3.2 Mamdani Fuzzy Models

The Mamdani type Fuzzy Inference System proposed by Mamdani and Assilian (Mamdani &

Assilian, 1975). The Mamdani Fuzzy Inference System is the most used methodology. The

benefits of the Mamdani Fuzzy Inference System usage are below (Hamam & Georganas,

2008).

• Expressive power,

• Straightforward formalization and interpretability,

• Reasonable results with a relatively simple structure,

• Intuitional and interpretable nature of the rule base,

• Can be both Multiple-Input-Single-Output (MISO) and Multiple-Input-Multiple-Output

(MIMO) systems,

• Output can either be fuzzy or crisp output.

The illustration of fuzzy conditional statements for the Mamdani Fuzzy Inference System is “if

𝑥𝑥1 is 𝐴𝐴1 and 𝑥𝑥2 is 𝐴𝐴2 and … and 𝑥𝑥𝑛𝑛 is 𝐴𝐴𝑛𝑛 then 𝑏𝑏 is 𝐵𝐵” (Jang, Sun, & Mizutani, 1997). The

linguistic variables are A and B, and X and Y are the universes of the discourse of the fuzzy

Generally, the Mamdani Fuzzy Inference System is based on the given inputs and the needed

output or outputs. The procedure of the Mamdani Fuzzy Inference System is illustrated below.

1. The fuzzification process of each input is completed by respect to the input membership

functions.

2. The fuzzy conditional statements’ set is determined.

3. The combination of the fuzzified inputs fulfils a rule strength establishment according

to the fuzzy conditional statements.

4. The combination of the rule strength and output membership functions helps to reach

the conclusions of the rule.

5. The conclusion combination is completed for obtaining an output distribution.

6. If the crisp output value is a need, the output distribution is defuzzified for finding the

crisp output.

4.3.3 Fuzzification

The fuzzification process is the first step of the Fuzzy Inference System. The transformation

from a crisp input value to a fuzzy input value is processed by selecting the membership

functions with corresponding degrees of membership in the fuzzification process (Jang, Sun, &

Mizutani, 1997). The transformation is completed at the beginning of the Fuzzy Inference

System process, and input values are translated into linguistic variables. The primary aim of

the fuzzification process is the usage of the inputs coming from a sensor set with values between

0 and 1, applying some input membership functions.

4.3.4 Knowledge Base

The knowledge base is another step of the fuzzy inference system. A rule base and a database

are the two components of the knowledge base. The rule base comprises the selection of fuzzy

conditional statements, and a database defines the membership functions in the fuzzy

conditional statements. The knowledge base is the core of the Fuzzy Inference System because

of a fuzzy reasoning and deductive mechanism. Also, the human brain has a specialized

knowledge-based system. The knowledge sources can be various, such as mathematical

methods and domain experts (Mamdani & Assilian, 1975).

4.3.5 Fuzzy Inference Engine

As discussed before, the fuzzy implication is the base of the fuzzy inference engine. The fuzzy

inference engine completes the generation of fuzzy conclusions from the knowledge base. The

fuzzy inference engine takes the fuzzified input variables and describes the conclusions by

evaluating the fuzzy conditional statements. Finally, all conclusions of each fuzzy conditional

statements produce the fuzzy output distribution.

4.3.6 Defuzzification

The defuzzification methodologies have used the production of the crisp output. The

defuzzification process is the way to convert from the fuzzy output to the crisp output if there

is a need for crisp output. Although there are five defuzzification methodologies, the Centroid

of Area and Mean of Maximum are the most used defuzzification techniques. In the equations,

the universe of discourse is Z, and a fuzzy set is A (Jang, Sun, & Mizutani, 1997). The detailed

explanation of defuzzification processes except for the Centroid of Area is shown in Appendix

B (page 143-144).

4.3.6.1 Centroid of Area

The most used methodology is the Centroid of Area, and the methodology gives accurate

results. The advantage of the Centroid of Area defuzzification method is that all activated

membership function of the conclusion takes part in the defuzzification process (Daftaribeshli,

Ataeri, & Sereshki, 2011). The Centroid of Area is explained in Equation 4.13 when 𝜇𝜇𝐴𝐴(𝑧𝑧) is

defined as the aggregated output membership function (Jang, Sun, & Mizutani, 1997).

𝑧𝑧𝐶𝐶𝐶𝐶𝐴𝐴 =

∫ 𝜇𝜇𝐴𝐴(𝑧𝑧)𝑧𝑧𝑎𝑎𝑧𝑧𝑍𝑍∫ 𝜇𝜇𝐴𝐴(𝑧𝑧)𝑎𝑎𝑧𝑧𝑍𝑍

, Equation 4.13

Application in MATLAB

4.4.1 The Fuzzy Logic Toolbox

As discussed before, the Fuzzy Logic is one of the most powerful techniques for dealing with

uncertainty and vagueness. The Fuzzy Logic design process comprises of fuzzifying inputs,

membership functions, the fuzzy conditional statements and defuzzifying the output

distribution. The design process is implemented easily by using the software toolbox, which is

the Fuzzy Logic Toolbox is developed by MATLAB (MATLAB, The MathWorks Inc, 1994-

2020). The Fuzzy Logic Toolbox is easy to run on personal computers using Microsoft

Windows, Apple Macintosh computers, and UNIX stations.

The Fuzzy Logic Toolbox provides a building function set in the computing environment of

MATLAB (Jang & Gulley, 1995). The toolbox is present to the help generation and editing of

the Fuzzy Inference System. The toolbox helps to solve problems for both Mamdani Fuzzy

Inference System and Sugeno Fuzzy Inference System, and the suitable approach for the

problems is accomplishable.

The Fuzzy Logic Toolbox is designed for an easy usage, and Graphical User Interface (GUI)

helps the users. There are five primary components in the Graphical User Interface: Fuzzy

Inference System Editor, Membership Function Editor, Rule Editor, Rule Viewer, and Surface

Viewer. The detailed explanation of components is shown in Appendix B (page 144-147).

4.4.2 The Fuzzy Logic Performance Evaluation

The designed Fuzzy Inference System is evaluated by using ‘evalfis’ function in the MATLAB

software. The function helps to train the designed system. The function takes the inputs values

and calculates the output value by using the Fuzzy Inference System. The formulation of the

function is expressed in Equation 4.14.

fis = readfis (‘FILE NAME’) Equation 4.14

output = evalfis (input, fis)

4.4.3 Application Designer

The Application Designer or App Designer supplies many tools in order to design new

applications in the MATLAB (Application Designer, 1994-2020). The App Designer creates a

development environment, which supplies layout views and code views. The designer

simplifies the process of the system design and simulates the system.

5. DESCRIPTION OF SYSTEM

Introduction

The diagnostic process of type 2 diabetes mellitus is time-consuming and costly. The proposed

system will help medical practitioners, healthcare authorities and patients. The successful

implementation of the system will reduce doctors' workload efficiently and effectively. The

system aims to reduce the excessive work burden of physicians and help them to provide fast

and effective care. Also, the patients will not pay more fees for the additional expensive tests.

After the successful establishment of the system, the decision-makers will assist physicians at

the prognosis phase, potentially preventing long term effects of diabetes mellitus. Moreover,

another key benefit is that it is high time to increase the decision capability of pharmacists,

nurses and medical practitioners while reducing the particular roles of doctors.

The system helps to diagnose type 2 diabetes mellitus in the early stage. Early diagnosis of type

2 diabetes mellitus will also help patients to save money and avoid the adverse effects of the

disease, such as becoming blind or losing some part of their bodies (arms, legs) or suffering

from other diseases. The system will increase patients’ life quality indirectly because the early

diagnosis and early therapy of disease will prevent complications.

The proposed system consists of 5 Fuzzy Inference Systems, two central Fuzzy Inference

Systems and three sub Fuzzy Inference Systems. The Primary Fuzzy Inference System works

with all three subsystems, but the Secondary Fuzzy Inference System just considers only two

sub Fuzzy Inference System out of three subsystems. The process flow chart is illustrated in

Figure 5.1, and the Fuzzy Inference Systems are shown in Figure 5.2.

Figure 5.1: The Diagnosis of Type II Diabetes Mellitus System's Flow Chart

VARIABLE

SELECTION

BIOLOGICAL FEATURES FIS

LIFESTYLE HABIT

KNOWN OR NOT?

PERSONAL FEATURES FIS

LIFESTYLE HABIT FIS

PERSONAL FEATURES FIS

BIOLOGICAL FEATURES FIS

PRIMARY TYPE II

DIAGNOSIS FIS

SECONDARY TYPE II

DIAGNOSIS FIS

PATIENT DIAGNOSIS RESULT

Figure 5.2: Fuzzy Inference Systems

INPUTS OUTPUTS

Gender

Family History

Nationality

Body-Mass

Systolic Blood

Pressure

Diastolic Blood

Pressure

Cholesterol

Blood Glucose

Level BIO

Dietary Factors

Smoking Habit

Alcohol

Consumption

Physical

Activity Level

Personal Features

Tendency

Biological Features

Tendency

Lifestyle Habit

Tendency

FINAL DECISION

SECONDARY SYSTEM

PATIENT‘S HEALTH

SITUATION

Primary Diagnosis Fuzzy Inference System Secondary Diagnosis Fuzzy Inference System

SUB FIS

MAIN FIS

Primary Diagnosis

Fuzzy Inference System

Secondary Diagnosis

Fuzzy Inference System

PRIMARY SYSTEM

PATIENT‘S HEALTH

SITUATION

The Overall System Operation Modes

The proposed overall system works under two operation modes. The significance of the modes

is to illustrate the Fuzzy Inference System process steps in diagnostic problems and to show the

processing differences between main diagnosis systems and sub-diagnosis systems. The

primary target to use these modes is to explain how the proposed model works and diagnose

the patients. The operation modes summarize the proposed model, the connection between the

user interface and the Fuzzy Inference System and to explain finding the output value through

the system. The Operation Mode-1 and Operation Mode-2 have represented the sub-diagnosis

system and the Central two systems, respectively. The operation modes are summarized in

Table 5-1.

Table 5-1: The System Operation Modes

Operation Mode-1 Operation Mode-2

1- To take the values of inputs from the patient or the system user in the user-interface.

To take the values of inputs from the sub-systems’ output results in the user-interface.

2- To make the necessary calculations for adjustment of the system (e.g. body-mass index).

If the lifestyle habit diabetes tendency’s output result is different from 0, use the Primary diagnosis system. If the value is 0, just diagnose using the Secondary diagnostic system.

The linguistic variables transfer to the membership functions. The membership functions for each input are chosen, and the degree of the membership functions are assigned.

To reflect each input risk level in the user-interface.

4- To match the inputs’ membership functions degree in the fuzzy conditional statements, and to find the output membership functions’ degree.

To match the inputs’ membership functions degree in the fuzzy conditional statements, and to find the output membership functions’ degree.

5- The output membership functions’ degree turns to linguistic variable and to save the linguistic variable for the Primary diagnosis system.

The output membership functions’ degree turns to linguistic variable and to illustrate in the result screen of user-interface, and to create suggestion screens for each result.

Definition of The System

The system will help the doctors for easy classification and diagnosis type 2 diabetes mellitus,

prediabetes (insulin resistance), and healthy patients. The Primary system and Secondary

system comprise of the three and two different subsystems, respectively. In total, the five

different Fuzzy Inference System are used.

The sub-systems are related to patients’ features, biological features, and lifestyle habits. The

systems show the relationships between inputs and outputs. Firstly, the Personal Features Sub-

System’s inputs are gender, age, family history, nationality, and body mass index, while the

output of the Personal Features Sub-System is the personal features tendency. Secondly, the

Biological Features Sub-System’s inputs are systolic blood pressure, diastolic blood pressure,

cholesterol level, blood glucose level and pregnancy situation, and the output of the Biological

Features Sub-System is the biological features tendency. Finally, in the Lifestyle Habit Sub-

System, inputs are physical activity level, dietary factors, smoking habit, and alcohol

consumption habit, whereas the output is the lifestyle tendency.

Moreover, the Primary system covers three different subsystems; while the Secondary system

is connected with only two different subsystems. The outputs of the subsystems are the inputs

to the Primary system and the Secondary system, while patients’ health situation is the output

of both the Primary and Secondary system. The detailed explanation of the systems is in Section

5.3.1, Section 5.3.2, Section 5.3.3, Section 5.3.4, and Section 5.3.5.

Membership functions’ type for each input and output in every system is triangular. The basic

formulas and computational efficiency of triangular membership function help efficiently

dealing with real-life implementations. Also, the triangular membership function gives more

accurate output results (Abdullah, Fadil, & Khairunizam, 2018). The ‘Center of Gravity’ has

been used as a defuzzification method. The fuzzy conditional statements have been created by

using expert knowledge through the literature review.

5.3.1 The Primary Type II Diabetes Prognosis Fuzzy Inference System

The inputs of diabetes diagnosis Fuzzy Inference System are the outputs of three subs Fuzzy

Inference Systems, while the Primary System patient’s health situation is the output. The inputs

are personal features of diabetes tendency, biological features diabetes tendency, and lifestyle

habits diabetes tendency. The system is in Figure 5.3.

The inputs’ membership functions categories are the same, and the categories are low risk,

medium risk, and high risk. The membership functions of the output are more diverse than the

inputs’ membership functions. The membership functions for the output are healthy (there is

no risk of diabetes mellitus), prediabetes or insulin resistance, and type 2 diabetes mellitus.

5.3.2 The Secondary Type II Diabetes Prognosis Fuzzy Inference System

The Intelligent System for the Diagnosis of type II diabetes mellitus uses only two different sub

Fuzzy Inference Systems. The published studies show that biological features and personal

features are some of the most significant elements for diagnosing type 2 diabetes mellitus.

Although lifestyle habits are another cause of illness, on some occasions, it is hard to find the

answer to the patient’s lifestyle behaviour.

The Secondary type II diabetes mellitus prognosis Fuzzy Inference System (Figure 5.4) helps

to diagnose patients without using their lifestyle habits. In the Fuzzy Inference System,

biological features and personal features are the inputs, and the output is the patients’ diagnosis

result. This system is an alternative way for the diagnosis of patients when there are missing

inputs information in the Primary system. This Secondary system neglects the significance of

lifestyle habits, so the future estimation of this system cannot be reliable for lifestyle habits;

especially when the patient has a lower biological and personal tendency.

Figure 5.3: The Primary Fuzzy Inference System for the Diagnosis of Type II Diabetes

Mellitus

Figure 5.4: The Secondary Fuzzy Inference System for the Diagnosis of Type II Diabetes

Mellitus

PERSONAL FEATURES

DIABETES TENDENCY

BIOLOGICAL

FEATURES DIABETES

TENDENCY

LIFESTYLE HABITS

DIABETES TENDENCY

DIAGNOSIS OF

TYPE II

DIABETES

MELLITUS

PRIMARY

SYSTEM

PATIENT’S

HEALTH

SITUATION

INPUTS OUTPUT THE PRIMARY SYSTEM

PERSONAL FEATURES

DIABETES TENDENCY

BIOLOGICAL

FEATURES DIABETES

TENDENCY

DIAGNOSIS OF

TYPE II

DIABETES

MELLITUS

SECONDARY

SYSTEM

PATIENT’S

HEALTH

SITUATION

INPUTS OUTPUT THE SECONDARY SYSTEM

5.3.3 Personal Features Type II Diabetes Tendency Fuzzy Inference System

In this Fuzzy Inference System, there are five inputs and one output. While gender, age, family

history, nationality, and body-mass index are inputs, the output is the personal features of

diabetes tendency. Each input and output have different numbers of membership functions. The

system is illustrated in Figure 5.5.

One of the inputs is the gender of patients. Female and Male are the two different kinds of

membership functions for gender (Table 5-2). Even though both genders have an equal

tendency, male people are at a slightly higher risk of developing type 2 diabetes mellitus than

female people (Sattar, 2013).

Five different age groups have used in the personal features diabetes tendency Fuzzy Inference

System. The age groups are between 20 and 29 years, between 30 and 39 years, between 40 and

49 years, between 50 and 59 years, and between 60 and older years. People under 19 years old,

and are out of this system because of their age range. Additionally, every age scale has a

different risk category, which is shown in Table 5-3. Every ethnicity has a different age

tendency, but the prevalence of type 2 diabetes mellitus increases drastically after 45 years old.

Moreover, another critical risk factor is a family history of patients. The impact of genetic

factors and genes has been proved in previous studies. Family history is an independent risk

factor (Hariri, et al., 2006). “Diabetes family history” and “genetic risk” are not synonymous.

The relationship between family history and risk of diabetes mellitus in the proband involves

the complex interplay between genes, shared environment, shared behaviours, and epigenetic

effects (Franks, 2010). The explanation of family history is shown in Figure 5.6, and the

membership functions are shown in Table 5-4.

Figure 5.5: Personal Features Type II Diabetes Mellitus Tendency

Figure 5.6: The Explanation of Family History for Type II Diabetes Mellitus Patients

(Franks, 2010)

INPUTS OUTPUT PERSONAL FEATURES

GENDER

FAMILY

HISTORY

NATIONALITY

BODY-MASS

PERSONAL FEATURES

TYPE II DIABETES

TENDENCY FUZZY

INFERENCE SYSTEM

PERSONAL

FEATURES

TYPE II

DIABETES

TENDENCY

In the personal features type II diabetes mellitus tendency Fuzzy Inference System, seven

different regions have used as a nationality of patients. The regions of the world are the

membership functions of the system. The effects of regions arrange according to current

patients amount and prevalence rate increases for fuzzy conditional statements in the system.

These regions are North America and the Caribbean, the Middle East and North Africa, Europe,

the Western Pacific, South-East Asia, Africa, and South and Central America. According to

International Diabetes Federation Reports (Cho, et al., 2018) (Guariguata L. , Whiting,

Hambleton, Linnenkamp, & Shaw, 2014) (Guariguata L. , Whiting, Weil, & Unwin, 2011)

(Ogurtsova, et al., 2017) (Shaw, Sicree, & Zimmet, 2010), Western Pacific has one of the

highest prevalence rates among the other regions. The patients, who live in the Western Pacific,

have a higher risk of suffering from type II diabetes mellitus. The lowest prevalence rate

belongs to Africa. African patients have a lower chance of diagnosing for type II diabetes

mellitus. The risk levels of the regions are shown in Table 5-5.

One of the primary causes of type 2 diabetes mellitus is the obesity/overweight. Obese and

overweight patients, whether active or inactive, have a higher risk than underweight and

normal-weight patients (Weinstein, et al., 2004). Once people become obese, their risk of type

2 diabetes mellitus increases drastically. In the personal features type 2 diabetes mellitus

tendency Fuzzy Inference System, there are four different body-mass indexes or weight

distribution. These four categories are underweight, normal-weight, overweight, and obese. The

limits of body mass index are displayed in Table 5-6.

In conclusion, the inputs are age, gender, nationality, family history, and body mass index.

Meanwhile, personal features diabetes tendency is the output in personal features diabetes

tendency Fuzzy Inference System. A total of 1400 fuzzy conditional statements have been used.

Table 5-2: Genders Membership Functions’ Risk Levels

Category Risk Level

Female Medium Risk

Male High Risk

Table 5-3: Age Membership Functions Level

Category Low Limit High Limit

20-29 Years Old 20 30

30-39 Years Old 29 40

40-49 Years Old 39 50

50-59 Years Old 49 60

60-Older Years Old 59 100

Table 5-4: Family History Membership Functions’ Risk Levels

Category Risk Level

No History/ Unknown History Low Risk

Just Mother Has Diabetes Low Risk

Just Father Has Diabetes Medium Risk

Both Parents Have Diabetes High Risk

Sibling(s) Has Diabetes Low Risk

Table 5-5: Nationality Membership Functions' Risk Levels

Category Risk Level

North America and the Caribbean High Risk

The Middle East and North Africa Medium Risk

Europe Medium Risk

Western Pacific High Risk

South-East Asia High Risk

Africa Low Risk

South and Central America Low Risk

Table 5-6: Body Mass Index Level

Category Low Limit High Limit

Underweight 0 18.4

Normal-Weight 18.5 24.9

Overweight 25 29.9

Obese 30 More

5.3.4 Biological Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

The biological features are a significant factor for the diagnosis of type 2 diabetes mellitus. The

Fuzzy Inference System for biological features diabetes tendency has five different inputs and

one output as in Figure 5.7. The numbers of membership functions are changeable according to

inputs and output, and there are 288 rules.

Blood pressure (systolic/diastolic) is the pressure of blood circulation on the walls of blood

vessels. Blood pressure level varies for every person. Both blood pressure levels are low,

medium, high, and very high. Blood pressure is the primary cause of cardiovascular diseases.

The classification of blood pressure categories is illustrated in Table 5-7. Blood pressure is one

of the biological factors for being type 2 diabetes mellitus. The reason behind the relationships

between blood pressure and type 2 diabetes mellitus is complications caused by type 2 diabetes

mellitus. Also, the scientific studies show that type 2 diabetes mellitus patients have mean blood

pressure levels (Kirk, Bell, Bertoni, Arcury, & Quandt, 2005). There is a link between high

blood pressure and glucose intolerance (Bonora, et al., 1987). If the blood pressure level is high,

the risk of type 2 diabetes mellitus will increase. The blood pressure values are displayed in

Table 5-8.

Cholesterol is a kind of lipid. Everybody has cholesterol needs to build healthy cells. However,

high levels of cholesterol will increase the risk of heart diseases. There is a link between

cholesterol levels and insulin resistance. Even though high-density lipid (HDL) has protective

indications, evidence shows that cholesterol may have a contribution to beta-cells dysfunction,

which increases the risk of type 2 diabetes mellitus (Brunham, Kruit, Hayden, & Verchere,

2010). The limits and membership functions of the total cholesterol level are illustrated in Table

One of the most critical decision parameters is the blood glucose level. The measurement of

blood glucose level can be possible by using methodologies of a fasting plasma glucose level,

a 2-hour plasma glucose value in a 75 g oral glucose tolerance test, and a glycated hemoglobin

value. American Diabetes Association defines the blood glucose level difference among healthy

patients, prediabetes, or insulin-resistant patients. According to American Diabetes

Association, blood glucose level, which is shown in Table 5-10, should be for healthy patients

which have a lower than 99 mg/dL; whereas, for prediabetes the patients have between 100

mg/dL and 125 mg/dL, and for type 2 diabetes mellitus the patients have 125 mg /dL.

The last input in the biological features diabetes tendency Fuzzy Inference System is the

pregnancy situation of patients. The question is that “Have you ever suffered from gestational

diabetes mellitus or pregnancy diabetes in any of pregnancy?” for understanding patients’ GDM

situation. If the patient suffered gestational diabetes mellitus in any of the pregnancy situations,

the risk of type 2 diabetes mellitus is high. As a result of the question of pregnancy, pregnancy

situation has two membership functions, which are positive and negative, in the Fuzzy Inference

System of biological features diabetes tendency. If the patient suffered from gestational diabetes

mellitus, their pregnancy situation in the system is positive; while if the patient did not suffer

from gestational diabetes mellitus, negative is the result of their pregnancy situation. The

previous studies in the literature show that if the patient has elevated fasting glucose or GDM

during the pregnancy, the risk of type 2 diabetes mellitus increases after delivery (Kim, Newton,

& Knopp, 2002). The risk levels are displayed in Table 5-11.

As a result, in the biological features diabetes tendency Fuzzy Inference System, there are five

inputs, which are blood glucose level, cholesterol level, systolic blood pressure level, diastolic

blood pressure, and pregnancy situation. The output is biological features diabetes tendency.

Figure 5.7: Biological Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

Table 5-7: Blood Pressure Categories (American Heart Association, 2018)

Blood Pressure Category Systolic Mm Hg

(Upper Number)

Diastolic Mm Hg

(Lower Number)

Normal LESS THAN 120 And LESS THAN 80

Elevated 120-129 And LESS THAN 80

High Blood Pressure

(Hypertension) Stage 1 130-139 Or 80-89

High Blood Pressure

(Hypertension) Stage 2 140 OR HIGHER Or 90 OR HIGHER

Hypertensive Crisis

(consult your doctor immediately) HIGHER THAN 180

HIGHER THAN

INPUTS OUTPUT BIOLOGICAL FEATURES

SYSTOLIC BLOOD

PRESSURE

CHOLESTEROL

BLOOD GLUCOSE

PREGNANCY

BIOLOGICAL FEATURES

TYPE II DIABETES

MELLITUS FUZZY

INFERENCE SYSTEM

BIOLOGICAL

FEATURES

DIABETES

TENDENCY

DIASTOLIC

BLOOD PRESSURE

Table 5-8: Blood Pressure's Membership Functions Limits

Membership Function Lower Limits Upper Limits

SYSTOLIC BLOOD PRESSURE

Low 0 mm Hg 120 mm Hg

Medium 119 mm Hg 130 mm Hg

High 129 mm Hg 140 mm Hg

Very High 139 mm Hg 300 mm Hg

DIASTOLIC BLOOD PRESSURE

Low 0 mm Hg 80 mm Hg

Medium 79 mm Hg 90 mm Hg

High 89 mm Hg 120 mm Hg

Very High 119 mm Hg 200 mm Hg

Table 5-9: Cholesterol's Membership Functions Limits

Membership Functions

Level Lower Limits Upper Limits

Low 0 mg/dL 200 mg/Dl

Medium 119 mg/dL 240 mg/dL

High 239 mg/dL 500 mg/dL

Table 5-10: Blood Glucose Levels for Membership Function

Membership Functions

Level Lower Limits Upper Limits

Low 0 mg/dL 100 mg/dL

Medium 99 mg/dL 126 mg/dL

High 125 mg/dL 800 mg/dL

Table 5-11: Pregnancy Situation Membership Functions' Risk Levels

Category Risk Level

Positive High Risk

Negative Low Risk

5.3.5 Lifestyle Habits Type II Diabetes Mellitus Tendency Fuzzy Inference System

There are four inputs and one output in the lifestyle habits diabetes tendency Fuzzy Inference

System in Figure 5.8. The inputs are physical activity level, dietary factors, smoking habits, and

alcohol consumption habits, while the lifestyle habits diabetes tendency is the output of the

Fuzzy Inference System. There are 192 fuzzy conditional statements in the system.

One of the inputs is the patients’ physical activity level. There is a strong relationship between

type 2 diabetes mellitus and the physical activity level of patients. According to medical

statistics, if the patients have a sedentary/ inactive lifestyle, the patients have a considerable

chance to suffer from type 2 diabetes mellitus. Also, the WHO’s statistics display that 1 out of

4 adults or 23% of adults are inactive globally (World Health Organization (WHO), 2018).

WHO recommends at least 150 minutes of moderate or 75 minutes of vigorous-intensity

activity per week. The input of the physical activity level has four membership functions. The

membership functions are sedentary or passive life, low activity or partial active, active, and

very active. Daily life activities or real working do not consider physical activity. The

categorization of physical activity levels is illustrated in Table 5-12.

Moreover, there are several types of dietaries and dietary factors in the world. Notably, an

unhealthy diet increases the risk of chronic diseases, and humans have a huge tendency to

follow unhealthy dietaries. The reason behind the increase in unhealthy dietary is modern

human life. In modern life, people have less time to cook and eat homemade foods. However,

a healthy diet should consist of eating more fruit, vegetables, legumes, nuts and grains, less salt,

sugar and fats (World Health Organization (WHO), 2018), and the person should not take

excessive calories in healthy diets (Hu, 2011). The membership functions are an unhealthier

diet, a partial healthier diet, and a healthier diet in Table 5-13.

There are many direct factors related to lifestyle habits for suffering from type 2 diabetes

mellitus, but indirect factors have a significance for the diagnosis process of type 2 diabetes

mellitus. The indirect factors are smoking and alcohol consumption of patients in the lifestyle

habits diabetes tendency Fuzzy Inference System. Bodyweight reduction is possible with a

smoking habit, but smoking influences the pancreatic beta-cells (Chang, 2012). The significant

interaction between smoking habits and type II diabetes mellitus has not been determined, but

smoking increases the insulin resistance of the patients. There are four membership functions,

which are never smoke, passive smoker, a former smoker, and active smoker. The amount of

usage affects the decision of membership functions. The explanation of membership functions

is presented in Table 5-14.

Another indirect factor is alcohol consumption. One of the ingredients in alcohol is sugar, so

alcohol comprises the significant reason for type 2 diabetes mellitus. However, low to moderate

drinking of alcohol have an impact on increasing insulin sensitivity (World Health Organization

(WHO), 2018). If alcohol consumption is more than a moderate level, it increases suffering

from diabetes mellitus. Heavy and excessive drinkers have higher type II diabetes mellitus

tendencies, and heavy drinkers’ insulin adherence is poor. Also, heavy drinkers’ motivation to

follow their treatment policy is lower than other people. Alcohol consumption has four

membership functions in the lifestyle habits diabetes tendency Fuzzy Inference System. The

membership functions are demonstrated in Table 5-15.

As a result of this, physical activity level, dietary factors, smoking habits, and alcohol

consumption habits are the inputs; while lifestyle habits diabetes tendency is the output of the

system in the lifestyle habits diabetes tendency Fuzzy Inference System.

Figure 5.8: Lifestyle Habits Type II Diabetes Mellitus Tendency Fuzzy Inference System

Table 5-12: The Categorization of Physical Activity Levels

Physical Activity Categories Activeness Level of Patient

Sedentary Less than 150 minutes moderate or 75 minutes vigorous

Low Activity 150 minutes moderate or 75 minutes vigorous

Active 300 minutes moderate-insensitivity

Very Active More than 300 minutes moderate-insensitivity

INPUTS OUTPUTS LIFESTYLE HABITS

DIETARY

FACTORS

SMOKING

PHYSICAL

ACTIVITY

ALCOHOL

CONSUMPTION

LIFESTYLE HABITS

TYPE II DIABETES

MELLITUS TENDENCY

FUZZY INFERENCE

SYSTEM

LIFESTYLE

HABITS

DIABETES

TENDENCY

Table 5-13: The Categorization of Dietary Factors

Dietary Factors

Categories Levels of Fast-Food Eating

Unhealthier Low fruits and vegetables, and high saturated fats and sugar

consumption

Partial Healthier Medium fruits and vegetables, and medium saturated fats and sugar

consumption

Healthier High fruits and vegetables, and less or no saturated fats and sugar

consumption

Table 5-14: The Membership Functions of Smoking Habit

Membership

Functions Smoking Level Risk Level

No Usage Not smoke or spend time in the smoking environment Low Risk

Passive Usage Not smoke but spend time in the smoking environment Medium Risk

Former Usage Not smoke now but smoked in past Medium Risk

Active Usage Smoke regularly High Risk

Table 5-15: The Membership Functions of Alcohol Consumption

Membership

Functions Alcohol Consumption Level

No Usage Never used alcohol Low

Low Usage Women, average ≤ 1 drink/day

Men, average ≤ 2 drinks/day

Regular

Women, average < 4 drinks/in occasion, average < 7 drinks/week

Men, average < 5 drinks/one occasion, average < 14 drinks/week

Addicted Women, average ≥ 4 drinks/one occasion, average ≥7 drinks/week

men, average ≥ 5 drinks/one occasion, average ≥14 drinks/week

The Application Design of Type II Diabetes Mellitus Diagnosis System

The proposed application is one of the original user-friendly possible designs. However, the

design becomes more complicated or simpler in order to last user desires. The application has

six primary screens, which are explained below. All screens have similar buttons: ‘Previous’,

‘Next’, and ‘Reset’. The buttons aim to turn the previous page by using the ‘Previous’ button,

to go to the next page and to evaluate the proposed features of the patient using the ‘Next’

button, and to turn to default settings of the criteria by using ‘Reset’ button.

• The first screen (Figure 5.9) is the ‘Welcome Screen’ in the application. The first screen

has explanations of the application. The explanations try to help users with an

understanding of the application and smooth implementation of the system. To start the

application, the user should push the ‘Start’ button on the welcome screen.

• The evaluation of the patient’s personal features is done in the second screen (Figure

5.10); the inputs are gender, age, family history, nationality, body mass index, and the

warning box. Gender, family history and nationality have their specific list box, and the

features’ information is mandatory for the proposed Fuzzy Inference System, diagnosis

of type 2 diabetes mellitus and the application. Also, age and body mass index of the

patient can enter manually by using Edit Field of each feature, and the patient’s age and

body mass index value have to enter by the user. The warning box shows the warnings

about given age and body mass index values if the value is outside of the range limits.

• The third screen, which evaluates the biological features (Figure 5.11), has blood

pressure level entry, cholesterol level entry, blood glucose level input and pregnancy

situation entry. The user enters the patient’s systolic blood pressure and diastolic blood

pressure, cholesterol level, blood glucose level, and pregnancy level using the list box.

If the patient is male or has not suffered from gestational diabetes mellitus, pregnancy

situation should be negative. Also, if the user gives value outside of the ranges, the

system will warn the user by the warning box.

• The fourth screen is the question screen (Figure 5.12). In this screen, the significant

separation between two central Fuzzy Inference Systems is illustrated. The screen asks

the user what does the user know about the patient’s lifestyle habit. If the user does not

know any patient’s lifestyle habit information, the user clicks the ‘NO’ button and

continues to the evaluation screen for diagnosing type 2 diabetes mellitus. On the

contrary, if the user knows one or more than one of the lifestyle habit information, the

user pushes the ‘YES’ button and proceeds to the lifestyle evaluation screen.

• The fifth screen (Figure 5.13) is designed for the patient’s lifestyle evaluation. Dietary

factors, physical activity levels, smoking habit and alcohol consumption of the patient

is evaluated in the fifth screen. There are list boxes for each criterion, and the user can

choose the patient’s lifestyle. However, the user does not know all of them or some of

them, the default value under the category of unknown information can be chosen. The

default value of each category is defined according to the mean value of each range.

• The last screen is the evaluation system (Figure 5.14), and the screen is the last step of

the application. In the fifth screen, there are result boxes and buttons. The result boxes

show the personal features risk level, biological features risk level, lifestyle habits risk

level, and the diagnostic result. The risk levels are calculated in the personal features,

biological features, and lifestyle habits screens by using developed Fuzzy Inference

Systems, respectively. The ‘Diagnose’ button completes the diagnostic process of the

patient by using the Primary Fuzzy Inference System. If the patient is healthier, the

result displays in green colour, and the result of type 2 diabetes mellitus or prediabetes

shows red colour. Also, a new evaluation is started by using the ‘New Patient’ button

while the application is closed by using the ‘Close’ button.

Figure 5.9: Welcome Page (MATLAB, The MathWorks Inc., 1994-2020)

Figure 5.10: Personal Features Screen (MATLAB, The MathWorks Inc., 1994-2020)

Figure 5.11: Biological Features Screen (MATLAB, The MathWorks Inc., 1994-2020)

Figure 5.12: Question Screen (MATLAB, The MathWorks Inc., 1994-2020)

Figure 5.13: Lifestyle Habits Screen (MATLAB, The MathWorks Inc., 1994-2020)

Figure 5.14: Evaluation Screen (MATLAB, The MathWorks Inc., 1994-2020)

The System Results

5.5.1 Performance Evaluation of the System

The intelligent system performance evaluates efficiently by analyzing real data in the system

result. The universal evaluation measures are specificity, sensitivity and accuracy, and the

measures evaluate the systems efficiently and effectively. Sensitivity measurement (Equation

5.2, (Benamina, Atmani, & Benbelkacem, 2018)) evaluates the diagnostic test correctly at

detecting a positive disease, while the specificity measurement (Equation 5.3, (Benamina,

Atmani, & Benbelkacem, 2018)) helps to evaluate how the percentage of the healthy patients,

who do not suffer from any disease, appropriately ruled out by the specificity measurement.

Accuracy (Equation 5.4, (Benamina, Atmani, & Benbelkacem, 2018)) is concluded using

specificity and sensitivity in the presence of prevalence.

Confusion Matrix, which is shown in Equation 5.1 (Benamina, Atmani, & Benbelkacem, 2018),

uses for specificity, sensitivity, and accuracy. The matrix has four primary elements: true

positive, true negative, false positive and false negative. True positive cases define as the

correctly classified number of data as belonging to the class interests, while the definition of

true negative cases is the correctly classified number of data as not belonging to the class

interests. Also, when the number of data misclassified as belonging to the class of interest, the

cases are called false positive. False-negative cases are the number of data misclassified as not

belonging to the class of interests.

𝐶𝐶𝑏𝑏𝑎𝑎𝑖𝑖𝑏𝑏𝐶𝐶𝑖𝑖𝑏𝑏𝑎𝑎 𝑀𝑀𝑎𝑎𝑚𝑚𝑏𝑏𝑖𝑖𝑥𝑥 = � 𝑇𝑇𝑏𝑏𝑏𝑏𝑡𝑡 𝑃𝑃𝑏𝑏𝐶𝐶𝑖𝑖𝑚𝑚𝑖𝑖𝑖𝑖𝑡𝑡 𝐹𝐹𝑎𝑎𝑖𝑖𝐶𝐶𝑡𝑡 𝑃𝑃𝑏𝑏𝐶𝐶𝑖𝑖𝑚𝑚𝑖𝑖𝑖𝑖𝑡𝑡𝐹𝐹𝑎𝑎𝑖𝑖𝐶𝐶𝑡𝑡 𝐴𝐴𝑡𝑡𝑘𝑘𝑎𝑎𝑚𝑚𝑖𝑖𝑖𝑖𝑡𝑡 𝑇𝑇𝑏𝑏𝑏𝑏𝑡𝑡 𝐴𝐴𝑡𝑡𝑘𝑘𝑎𝑎𝑚𝑚𝑖𝑖𝑖𝑖𝑡𝑡�, Equation 5.1

𝑆𝑆𝑡𝑡𝑎𝑎𝐶𝐶𝑖𝑖𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑏𝑏 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇+𝐹𝐹𝑎𝑎𝐹𝐹𝑃𝑃𝑇𝑇 𝑁𝑁𝑇𝑇𝑁𝑁𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇

, Equation 5.2

𝑆𝑆𝑆𝑆𝑡𝑡𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑏𝑏 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑁𝑁𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑁𝑁𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇+𝐹𝐹𝑎𝑎𝐹𝐹𝑃𝑃𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇

, Equation 5.3

𝐴𝐴𝑚𝑚𝑚𝑚𝑏𝑏𝑏𝑏𝑎𝑎𝑚𝑚𝑏𝑏 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑁𝑁𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇 +𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑁𝑁𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇+𝐹𝐹𝑎𝑎𝐹𝐹𝑃𝑃𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇 +𝐹𝐹𝑎𝑎𝐹𝐹𝑃𝑃𝑇𝑇 𝑁𝑁𝑇𝑇𝑁𝑁𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇

, Equation 5.4

5.5.2 Simulation and Analysis of Results

In order to test the proposed overall Fuzzy Inference System: the Randomized Diabetes Dataset;

the BioStatistics Diabetes Dataset (Department of BioStatistics, Vanderblit University, 2019);

and the Pima Indian Diabetes Dataset (Dua & Graff, 2019). These data sets are presented for

the evaluation of the proposed model results, as shown in Appendix B. Also, the comparison

between different dataset groups is illustrated in Figure 5.15.

The verification process of the model is completed by using the three different databases

mentioned above. Three different sub-datasets, each with 100 randomized data, are created

from these databases. Each patient information from the sub-databases, especially PIDD and

BSDD, have been chosen randomly and took approval from doctors for usage in the system.

The randomized input data is approved by physicians. The first part of the proposed system,

which is covered by three subsystems, is tested with randomized produced patient data because

the randomized dataset helps to test the first part of the proposed system. In the verification

process, if no error occurred, it means that the proposed system works and diagnoses the patient

accurately. If any mistaken diagnosis occurs, the feedback, if necessary, from the beginning of

the building membership functions’ parameters to the fuzzy conditional statements should

check. The feedback process helps better and more accurate design and results from the

intelligent system. The confusion matrix of the randomized data is in Table 5-16.

Pima Indian Diabetes Dataset comes from Pima Indian people in the North America and

Caribbean region and the Native American community is known with the highest prevalence

rate of type 2 diabetes mellitus and other diseases. The dataset has 768 patients with eight

different attributes, which are numbers of pregnancy, plasma glucose concentration, diastolic

blood pressure, skin thickness level, insulin, body mass index, diabetes predigree function and

age. In the dataset, 268 patients out of 768 patients indicated as a type 2 diabetes mellitus

patient. Randomized chosen 100 data from Pima Indian Diabetes Dataset is tried with six

attributes, which are gender (Female), diastolic blood pressure, age, body mass index, plasma

glucose concentration and nationality. The system sensitivity, specificity and accuracy are

shown in Table 5-17. The result of the Pima Indian Diabetes Dataset shows that the proposed

system could not diagnose patients accurately with missing inputs in personal information and

biological information.

By the same token, another step of the verification process is completed by using 100

randomized chosen patient information from BioStatistics Diabetes Dataset. The dataset covers

403 patients information with 19 attributes (patient id, total cholesterol, stabilized glucose,

high-density lipoprotein, ratio of cholesterol and HDL levels, glycosylated hemoglobin,

location, age, gender, height, weight, frame, first systolic blood pressure, first diastolic blood

pressure, second systolic blood pressure, second diastolic blood pressure, waist, hip and

postprandial time when labs were drawn) from Buckingham and Louisiana. The differences

between Pima Indian Diabetes Dataset and BioStatistics Diabetes Dataset are the number of

attributes and the location of the people. The calculated sensitivity, specificity and accuracy are

displayed in Table 5-18. The proposed system performance evaluation criteria’s value

increases, but missing inputs continue to reduce the performance of the proposed system.

Figure 5.15: Dataset Comparison for The Proposed System

69.20%

93.75%96%

92.50%

98.33%97% 95.86%100%

Accuracy Specificity Sensitivity

Performance Evaluation Criteria

Dataset Performance Evaluation Criteria Comparison

Pima Indian Diabetes Dataset BioStatistics Diabetes Dataset Randomized Diabetes Dataset

Table 5-16: Randomized Diabetes Dataset Performance Evaluation

CONFUSION MATRIX

TRUE FALSE

POSITIVE NEGATIVE POSITIVE NEGATIVE

27 cases 70 cases 3 cases 0 case

ACCURACY SPECIFICITY SENSITIVITY

97% 95.89% 100%

Table 5-17: Pima Indian Diabetes Dataset Performance Evaluation

CONFUSION MATRIX

TRUE FALSE

45 cases 36 cases 16 cases 3 cases

81% 69.2% 93.75%

Table 5-18: BioStatistiscs Diabetes Dataset Performance Evaluation

CONFUSION MATRIX

TRUE FALSE

59 cases 37 cases 3 cases 1 case

96% 92.5% 98.33%

As previously mentioned, there are three different groups of the dataset for validation of the

proposed methodology. The numbers of attributes for the randomized production dataset, the

Pima Indian Diabetes Dataset, and BioStatistics Diabetes Dataset are 14, 6 and 8, respectively.

The results and solutions only rely on expert knowledge in the intelligent system. The results

are analyzed as follows.

• For Pima Indian Diabetes Dataset, the proposed system gives 81% accurate results using

six attributes. Also, 69.2% specificity means that approximately 70% of healthier people

have identified correctly, and the sensitivity rate shows that 93.75% of prediabetes and

type 2 diabetes mellitus patients have been classified correctly by the proposed system.

The lower specificity for PIDD shows that when the number of attributes meets the

system requirements, the proposed system may diagnose some healthier patients

mistakenly.

• When the proposed system is tried with more attributes; accuracy, specificity, and

sensitivity rates have increased drastically. The diagnosis of type 2 diabetes mellitus by

utilizing the BioStatistics Diabetes Dataset (8 attributes) is feasible with 96%

accurately. 98.33% of prediabetes and type 2 diabetes mellitus patients are diagnosed

while the healthier participants’ detection rate is 92.5%.

• Finally, Randomized Diabetes Dataset helped to verify the proposed system with 14

attributes, and the performance of the system showed a boosting trend. The performance

rates of the proposed system are 97% for accuracy, 95.89% for specificity and 100% for

sensitivity.

The results prove that the Fuzzy Logic methodology is beneficial for medical and real-life

application; because fuzzy variables, fuzzy conditional statements (rules for diagnosis), and the

developed system can be easily updated and modified for better analysis. The proposed

methodology and system can utilize efficiently in clinical applications for fast and inexpensive

diagnosis. Also, the summary of medical knowledge in the fuzzy conditional statements and

fuzzy variables will help further developments in the area.

6. CONCLUSIONS

System Review

The primary concern of the Thesis is diagnosing problems. Throughout the various Intelligence

System techniques analysis, the study proposes a Fuzzy Logic methodology to generate an

intelligent Fuzzy Inference Systems system.

Firstly, the literature review of the related works is completed in Chapter 2. The studies from

2010 to 2019 are explained. The many intelligence system techniques that have dealt with the

healthcare problems are briefly discussed. The systematic review consists of global healthcare

systems and intelligent systems, and diabetes mellitus and intelligent systems.

The Chapter 3 presents Diabetes Mellitus (DM), as well as historical developments, type of

diabetes mellitus, symptoms, diagnosis criteria of the disease and challenges of the diagnosis

process in the disease.

Next, the proposed methodology, based on Fuzzy Logic, is presented in Chapter 4. A Fuzzy

Inference System has become an essential part of intelligent decision making because of its

ability to deal with the uncertainty that regularly occurs in the decision-making process. In this

chapter, the foundations of the Fuzzy Inference System are presented, including the Fuzzy Set

Theory, Fuzzy Conditional Statements, and Fuzzy Reasoning. Also, the Fuzzy Logic Toolbox

used for the Fuzzy Inference System design, analysis, and implementation is described.

Finally, the proposed study is explained in Chapter 5. The study considers five different fuzzy

inference systems: the Primary Fuzzy Inference System; the Secondary Fuzzy Inference

System; and three sub-systems, namely: personal features diabetes mellitus tendency;

biological features diabetes mellitus tendency; and lifestyle habit diabetes mellitus tendency.

The total amount of inputs to the sub-systems is 14; while the total number of outputs is one for

each sub-system. The three sub Fuzzy Inference Systems’ outputs are the inputs in the Primary

and Secondary Fuzzy Inference Systems. Also, in this chapter the user-friendly application is

detailed.

In conclusion, the proposed methodology presents an effective and efficient diagnostic system

for diabetes mellitus disease with the use of the Fuzzy Logic Toolbox of MATLAB. The Fuzzy

Inference System operation for diagnostic of diabetes mellitus can be shown through a

Graphical User Interface (GUI), which interacts between a user and the Fuzzy Logic system.

Main Contribution of the Thesis

The main aim of the Thesis is to assist doctors in the diagnosis of prediabetes and type II

diabetes mellitus with the help of an intelligent system. The proposed system classifies healthy,

prediabetic and type II diabetic patients with accurate results, which are 97% for RPD, 96% for

BSDD and 81% for PIDD. These results explain that the proposed system could assist doctors

and help to reduce undiagnosed cases. Also, the results prove that the objectives of the research

have met.

The significant differences from the previous studies are: (1) diagnosing prediabetes and type

II diabetes mellitus by the same system; and (2) considering more variables for the diagnostic

problem. The performance comparison of the proposed system and the previous studies prove

that the proposed system gives a more accurate result by using more variables for diagnosing

prediabetes and type II diabetes mellitus. Thus, the proposed system could be used in clinical

applications efficiently and effectively. Also, the primary contributions of the Thesis are

displayed below.

• The Thesis work develops a successful application of a diabetes mellitus diagnostic for

Type 2 Diabetes Mellitus patients. The system uses a different paradigm with its inputs

and outputs that have not been considered in the previous literature studies.

• The Thesis affirms once again that the Fuzzy Logic methodology is an essential and

useful development for diagnostic problems. However, as shown in this Thesis, the

Fuzzy Logic can be developed for the effective diagnostic of Type 2 Diabetes Mellitus.

• The User-Friendly Application is designed, and the application creates a suitable user-

friendly interface that allows users a natural interaction with the developed intelligent

system.

• The Thesis proves that the developed intelligent system can be used for diagnostic

problems and apply to real-life clinical applications.

Future Work

The future application developments of the proposed system are essential, and the

improvements increase the efficiency of the proposed system. Future developments are

summarized below.

• The system has limits to evaluate the patients. One of the main barriers is additional

information, which can be collected by the medical practitioners in the patient file.

The medical practitioners may have advantages when diagnosing Type 2 Diabetes

Mellitus. Thus, to make the system more useful, the number of inputs should

increase in order to include more information about the patient in the diagnostic

process.

• The fine-tuning techniques should use for optimizing the membership function

parameters concerning performance criteria. The usage of fine-tuning techniques

can help the Fuzzy Inference System to become more advanced.

• The system could be developed to become more abundant and colourful in

diagnosing other types of diabetes mellitus, such as Type 1 Diabetes Mellitus and

Gestational Diabetes Mellitus.

• The knowledge base of the system should be improved for performance analysis.

• The user-friendly applications could be improved to be more user-friendly and to

use with computers without MATLAB software.

The expectation is that the diagnostic methodology can be a beneficial and competitive

technique for diagnosing medical problems. The new methodologies will be a considerable

development and of significant importance for their utilization in future clinical

applications.

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APPENDICES

APPENDIX A: DIABETES MELLITUS

Table A-1: Top 10 Countries for Diabetes Mellitus Prevalence in 2010 and 2030 (Shaw, Sicree,

& Zimmet, 2010)

2010 2030

Country Prevalence (%) Country Prevalence (%)

1 Nauru 30.9 Nauru 33.4

2 United Arab

Emirates 18.7

United Arab

Emirates 21.4

3 Saudi Arabia 16.8 Mauritius 19.8

4 Mauritius 16.2 Saudi Arabia 18.9

5 Bahrain 15.4 Reunion 18.1

6 Reunion 15.3 Bahrain 17.3

7 Kuwait 14.6 Kuwait 16.9

8 Oman 13.4 Tonga 15.7

9 Tonga 13.4 Oman 14.9

10 Malaysia 11.6 Malaysia 13.8

Table A-2: Top 10 Countries for Numbers of People Aged 20-79 Years with Diabetes Mellitus

in 2010 and 2030 (Shaw, Sicree, & Zimmet, 2010)

2010 2030

Country

#of Adults with

Diabetes

(millions)

Country

#of Adults with

Diabetes

(millions)

1 India 50.8 India 87.0

2 China 43.2 China 62.6

3 USA 26.8 USA 36.0

4 Russian

Federation 9.6 Pakistan 13.8

5 Brazil 7.6 Brazil 12.7

6 Germany 7.5 Indonesia 12.0

7 Pakistan 7.1 Mexico 11.9

8 Japan 7.1 Bangladesh 10.4

9 Indonesia 7.0 Russian

Federation 10.3

10 Mexico 6.8 Egypt 8.6

Table A-3: The Prevalence of Diabetes Mellitus and Estimated Diabetes Mellitus Numbers

Among Adults Aged 20-79 years for the years 2010 and 2030: 80 Most Populous Countries

(Shaw, Sicree, & Zimmet, 2010)

Country

Prevalence (%) Adjusted To #Adults with

Diabetes (000s) Mean

Annual

Increment

(000s)

World Population National

Population 2010 2030

2010 2030 2010 2030

AFRICA: SUB-SAHARA

Angola 3.5 4.7 2.8 3.5 224 506 14

Burkina

Faso 3.8 4.6 3.0 3.5 209 470 13

Cameroon 3.9 4.8 4.4 4.8 415 745 16

d’Ivoire 4.7 5.5 4.0 4.4 394 713 16

Dem. Rep. of

Congo 3.2 4.4 2.6 3.2 743 1760 51

Ethiopia 2.5 3.5 2.0 2.8 826 2031 60

Ghana 4.3 5.2 3.6 4.3 458 896 22

Kenya 3.5 4.7 2.8 3.7 519 640 36

Madagascar 3.2 4.4 2.7 3.5 270 266 18

Malawi 2.3 3.3 1.8 2.3 115 585 8

Mozambique 4.0 5.1 3.3 3.7 329 499 13

Niger 3.9 4.7 3.4 3.7 224 5316 14

Nigeria 4.7 5.5 3.9 4.3 2819 1231 125

Senegal 4.7 5.6 4.0 4.5 256 503 12

South Africa 4.5 5.6 4.5 4.9 1283 1644 18

Uganda 2.2 3.1 1.7 2.2 224 617 20

Tanzania 3.2 4.3 2.6 3.3 504 1155 33

Zimbabwe 4.1 5.3 3.4 4.0 235 389 8

Bangladesh 6.6 7.9 6.1 7.4 5681 10423 237

Cambodia 5.2 6.5 4.3 5.6 354 724 19

China 4.2 5.0 4.5 5.8 43157 62553 970

Dem. Rep. of

Korea 5.3 6.2 5.7 6.8 943 1256 16

India 7.8 9.3 7.1 8.6 50768 87036 1813

Indonesia 4.8 5.9 4.6 6.0 6964 11980 251

Japan 5.0 5.9 7.3 8.0 7089 6879 11

Malaysia 11.6 13.8 10.9 13.4 1846 3245 70

Myanmar 3.2 4.3 2.8 4.3 922 1755 42

Nepal 3.9 5.2 3.3 4.2 511 1070 28

Philippines 7.7 8.9 6.7 7.8 3398 6164 138

Republic of

Korea 7.9 9.0 9.0 11.4 3292 4323 52

Sri Lanka 10.9 13.5 11.5 14.9 1529 2158 31

Taiwan 7.5 8.5 5.7 6.8 816 1232 21

Thailand 7.1 8.4 7.7 9.8 3538 4956 71

Viet Nam 3.5 4.4 2.9 4.4 1647 3415 88

EUROPE/ NORTH AMERICA/ OCENIA

Australia 5.7 6.8 7.2 8.4 1086 1503 21

Belarus 7.6 9.0 9.1 11.1 661 725 3

Belgium 5.3 6.7 8.0 9.6 610 750 7

Canada 9.2 10.9 11.6 13.9 2866 3981 56

Republic 6.4 7.8 8.7 10.7 677 793 6

France 6.7 8.3 9.4 11.0 4164 5201 52

Germany 8.9 10.2 12.0 13.5 7494 8014 26

Greece 6.0 7.4 8.8 10.3 754 875 6

Hungary 6.4 7.8 8.8 10.3 659 727 3

Italy 5.9 7.2 8.8 10.4 3926 4483 28

Netherlands 5.3 6.7 7.7 9.5 922 1178 13

Poland 7.6 9.0 9.3 11.6 2675 3153 24

Portugal 9.6 11.2 12.2 14.4 978 1143 8

Romania 6.9 8.0 8.4 10.0 1351 1469 6

Russian

Federation 7.6 9.0 9.0 10.9 9625 10330 35

Serbia 6.9 8.0 8.6 9.5 613 687 4

Spain 6.6 8.0 8.7 11.1 2939 3866 46

Sweden 5.2 6.2 7.3 8.0 484 556 4

Ukraine 7.6 9.0 9.6 11.3 3328 3349 1

United

Kingdom 3.6 4.3 4.9 5.4 2140 2549 20

USA 10.3 12.0 12.3 14.0 26814 35958 457

LATIN AMERICA/ CARIBBEAN

Argentina 5.7 6.5 6.0 6.6 1558 2158 30

Brazil 6.4 7.7 6.0 7.8 7633 12708 254

Chile 5.7 6.5 6.1 7.2 699 1006 15

Colombia 5.2 6.2 4.8 6.3 1427 2506 54

Cuba 99.5 10.9 11.0 13.5 903 1143 12

Ecuador 5.9 7.1 5.5 6.8 443 753 15

Guatemala 8.6 10.6 6.9 8.0 465 983 26

Mexico 10.8 12.9 10.1 13.3 6827 11910 254

Peru 6.2 7.3 5.6 7.0 962 1666 35

Venezuela 6.5 7.8 5.9 7.4 1034 1840 40

MIDDLE-EAST CRESCENT

Afghanistan 8.6 9.9 6.6 7.0 856 1726 43

Algeria 8.5 9.4 7.4 9.3 1632 2850 61

Egypt 11.4 13.7 10.4 12.8 4787 8615 191

Iran (Islamic

Rep. of) 8.0 9.8 6.1 9.3 2872 5981 155

Iraq 10.2 7.0 5.6 9.3 1176 2605 71

Kazakhstan 5.8 9.8 7.6 7.1 584 843 13

Morocco 7.3 10.5 7.6 9.7 1513 2589 54

Pakistan 9.1 18.9 13.6 9.3 7146 13833 334

Arabia 16.8 18.9 13.6 17.0 2065 4183 106

Sudan 4.2 5.2 3.3 4.0 675 1367 35

Syrian Arab

Republic 10.8 13.2 8.3 11.0 974 2099 56

Tunisia 9.3 11.0 8.5 11.7 602 1052 22

Turkey 8.0 9.4 7.4 9.6 3679 6323 132

Uzbekistan 5.2 6.6 4.0 5.8 674 1407 37

Yemen 3.0 3.5 2.5 2.9 270 622 18

Table A-4: The Projections of General Population, Diabetes Mellitus and Health Expenditures

for 2015 and 2040 (Ogurtsova, et al., 2017)

2015 2040

GENERAL POPULATION

Total World Population 7.3 billion 9.0 billion

Adult Population

(20-79 years) 4.7 billion 6.2 billion

DIABETES (20-79 YEARS)

Global Prevalence

(uncertainty interval)

(7.2 – 11.4%)

(8.5 – 13.5%)

Number of People with

Diabetes

(uncertainty interval)

415 million

(340 – 536 million)

642 million

(521 – 829 million)

Number of Deaths Due to

Diabetes 5.0 million Not Calculated

HEALTH EXPENDITURE DUE TO DIABETES (20-79 YEARS)

Total Health Expenditure,

2015 USD 673 billion 802 billion

Table A-5: Criteria for The Diagnosis of Diabetes Mellitus (American Diabetes Association,

Fasting Plasma Glucose (FPG)

≥ 126 mg/dL (7.0 mmol/L). Fasting defines as no caloric

intake for at least 8 hours (in the absence of unequivocal

hyperglycemia, results should confirm by repeat testing).

2 Hour Plasma Glucose (2-h PG)

≥ 200 mg/dL (11.1 mmol/L) during OGTT. The test

should perform as described by the World Health

Organization, using a glucose load containing the

equivalent of 75-g anhydrous glucose dissolved in water

(in the absence of unequivocal hyperglycemia, results

should confirm by repeat testing).

Glycated Hemoglobin (A1C)

≥ 6.5% (48 mmol/mol). The test should perform in a

laboratory using a method that is National

Glycohemoglobin Standardization Program (NGSP)

certified and standardized to the Diabetes Control and

Complications Trial (DCCT) assay (in the absence of

unequivocal hyperglycemia, results should confirm by

repeat testing).

Random Plasma Glucose ≥ 200 mg/dL (11.1 mmol/mol). In a patient with classic

symptoms of hyperglycemia or hyperglycemic crisis.

Table A-6: Advantages and Disadvantages of Diagnostic Tests for Diabetes Mellitus

(Goldenberg & Punthakee, 2013)

PARAMETER ADVANTAGES DISADVANTAGES

Fasting Plasma

Glucose (FPG)

• Established standard

• Fast and easy

• Single sample

• Predicts microvascular

complications

• Sample not stable

• High day-to-day variability

• Inconvenient (fasting)

• Reflects glucose homeostasis at

a single point in time

2 Hour Plasma

Glucose (2hPG) in

a 75 g Oral

Glucose Tolerance

Test (OGTT)

• Established standard

complications

• Sample not stable

• High day-to-day variability

• Inconvenient

• Unpalatable

• Cost

A Glycated

Hemoglobin (A1C)

• Convenient (measure any

time of day)

• Single sample

complications

• A better predictor of

macrovascular disease than

FPG or 2hPG in a 75 g

• Low day-to-day variability

• Reflects long-term glucose

concentration

• Cost

• Misleading in various medical

conditions (e.g.

hemoglobinopathies, iron

deficiency, hemolytic anemia,

severe hepatic, or renal disease)

• Altered by ethnicity and ageing

• Standardized, validated assay

required

• Not for diagnostic use in

children, adolescents, pregnant

women, or those with suspected

type 1 diabetes mellitus

APPENDIX B: FUZZY SET THEORY

Table B-1: Basic Identities of Classical Sets, where A, B, and C are crisp sets; A�, B�, and C� are

their corresponding complements; X is the universe; and ∅ is the empty set (Jang, Sun, &

Mizutani, 1997)

Law of Contradiction 𝐴𝐴 ∩ 𝐴𝐴̅ = ∅

Law of The Excluded Middle 𝐴𝐴 ∪ 𝐴𝐴̅ = 𝑋𝑋

Idempotency 𝐴𝐴 ∩ 𝐴𝐴 = 𝐴𝐴,𝐴𝐴 ∪ 𝐴𝐴 = 𝐴𝐴

Involution 𝐴𝐴̿ = 𝐴𝐴

Commutativity 𝐴𝐴 ∩ 𝐵𝐵 = 𝐵𝐵 ∩ 𝐴𝐴,𝐴𝐴 ∪ 𝐵𝐵 = 𝐵𝐵 ∪ 𝐴𝐴

Distributivity 𝐴𝐴 ∪ (𝐵𝐵 ∩ 𝐶𝐶) = (𝐴𝐴 ∪ 𝐵𝐵) ∩ (𝐴𝐴 ∪ 𝐶𝐶)

𝐴𝐴 ∩ (𝐵𝐵 ∪ 𝐶𝐶) = (𝐴𝐴 ∩ 𝐵𝐵) ∪ (𝐴𝐴 ∩ 𝐶𝐶)

Absorption 𝐴𝐴 ∪ (𝐴𝐴 ∩ 𝐵𝐵) = 𝐴𝐴

𝐴𝐴 ∩ (𝐴𝐴 ∪ 𝐵𝐵) = 𝐴𝐴

Absorption of Complement 𝐴𝐴 ∪ (𝐴𝐴̅ ∩ 𝐵𝐵) = 𝐴𝐴

𝐴𝐴 ∩ (𝐴𝐴̅ ∪ 𝐵𝐵) = 𝐴𝐴

DeMorgan’s Laws 𝐴𝐴 ∪ 𝐵𝐵�� = 𝐴𝐴̅ ∩ 𝐵𝐵�

𝐴𝐴 ∩ 𝐵𝐵�� = 𝐴𝐴̅ ∪ 𝐵𝐵�

T-NORM

There are four T-norm operators: boundary, monotonicity, commutativity, and associativity.

The boundary requirement enforces the correct generalization to crisp sets. Monotonicity

requirement implies that a decrease in the membership values in A or B cannot produce an

increase in the membership value in 𝐴𝐴 ∩ 𝐵𝐵. The commutativity requirement indicates the

combination of the fuzzy sets’ operators' indifference. The allowance of associativity

requirement is to take any number of sets intersection in any order of pairwise groupings.

𝑇𝑇 (0,0) = 0, 𝑇𝑇 (𝑎𝑎, 1) = 𝑇𝑇 (1, 𝑎𝑎) = 𝑎𝑎 (𝑏𝑏𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎𝑎𝑎𝑏𝑏𝑏𝑏)

𝑇𝑇(𝑎𝑎,𝑏𝑏) ≤ 𝑇𝑇(𝑚𝑚,𝑎𝑎) 𝑖𝑖𝑖𝑖 𝑎𝑎 ≤ 𝑚𝑚 𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏 ≤ 𝑎𝑎 (𝑚𝑚𝑏𝑏𝑎𝑎𝑏𝑏𝑚𝑚𝑏𝑏𝑎𝑎𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑏𝑏)

𝑇𝑇(𝑎𝑎,𝑏𝑏) = 𝑇𝑇(𝑏𝑏,𝑎𝑎) (𝑚𝑚𝑏𝑏𝑚𝑚𝑚𝑚𝑏𝑏𝑚𝑚𝑎𝑎𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑏𝑏)

𝑇𝑇(𝑎𝑎,𝑇𝑇(𝑏𝑏, 𝑚𝑚)) = 𝑇𝑇(𝑇𝑇(𝑎𝑎, 𝑏𝑏), 𝑚𝑚) (𝑎𝑎𝐶𝐶𝐶𝐶𝑏𝑏𝑚𝑚𝑖𝑖𝑎𝑎𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑏𝑏)

T-CONORM

There are four T-conorm operators: boundary, monotonicity, commutativity, and associativity.

The four T-conorm operators are in the following (Jang, Sun, & Mizutani, 1997).

𝑆𝑆 (1,1) = 1,𝑆𝑆 (0,𝑎𝑎) = 𝑆𝑆 (𝑎𝑎, 0) = 𝑎𝑎 (𝑏𝑏𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎𝑎𝑎𝑏𝑏𝑏𝑏)

𝑆𝑆(𝑎𝑎, 𝑏𝑏) ≤ 𝑇𝑇(𝑚𝑚,𝑎𝑎) 𝑖𝑖𝑖𝑖 𝑎𝑎 ≤ 𝑚𝑚 𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏 ≤ 𝑎𝑎 (𝑚𝑚𝑏𝑏𝑎𝑎𝑏𝑏𝑚𝑚𝑏𝑏𝑎𝑎𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑏𝑏)

𝑆𝑆(𝑎𝑎,𝑏𝑏) = 𝑆𝑆(𝑏𝑏,𝑎𝑎) (𝑚𝑚𝑏𝑏𝑚𝑚𝑚𝑚𝑏𝑏𝑚𝑚𝑎𝑎𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑏𝑏)

𝑆𝑆(𝑎𝑎,𝑆𝑆(𝑏𝑏, 𝑚𝑚)) = 𝑆𝑆(𝑆𝑆(𝑎𝑎,𝑏𝑏), 𝑚𝑚) (𝑎𝑎𝐶𝐶𝐶𝐶𝑏𝑏𝑚𝑚𝑖𝑖𝑎𝑎𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑏𝑏)

MEMBERSHIP FUNCTIONS Trapezoidal Membership Functions

A trapezoidal membership function uses four parameters, which are a, b, c, and d. Trapezoidal

membership functions’ parameters present the four corners of the x-axis. The alignments of the

parameters are 𝑎𝑎 < 𝑏𝑏 ≤ 𝑚𝑚 < 𝑎𝑎. The expressions of the trapezoidal membership functions are

in the following (Jang, Sun, & Mizutani, 1997).

𝑚𝑚𝑏𝑏𝑎𝑎𝑆𝑆𝑡𝑡𝑧𝑧𝑏𝑏𝑖𝑖𝑎𝑎 (𝑥𝑥; 𝑎𝑎,𝑏𝑏, 𝑚𝑚,𝑎𝑎) =

⎩⎪⎪⎨

⎪⎪⎧

0, 𝑥𝑥 ≤ 𝑎𝑎𝑥𝑥 − 𝑎𝑎𝑏𝑏 − 𝑎𝑎

, 𝑎𝑎 ≤ 𝑥𝑥 ≤ 𝑏𝑏

1, 𝑏𝑏 ≤ 𝑥𝑥 ≤ 𝑚𝑚𝑎𝑎 − 𝑥𝑥𝑎𝑎 − 𝑚𝑚

, 𝑚𝑚 ≤ 𝑥𝑥 ≤ 𝑎𝑎

0, 𝑎𝑎 ≤ 𝑥𝑥

𝑚𝑚𝑏𝑏𝑎𝑎𝑆𝑆𝑡𝑡𝑧𝑧𝑏𝑏𝑖𝑖𝑎𝑎 (𝑥𝑥; 𝑎𝑎, 𝑏𝑏, 𝑚𝑚,𝑎𝑎) = max �min �𝑥𝑥−𝑎𝑎𝑏𝑏−𝑎𝑎

, 𝑑𝑑−𝑥𝑥𝑑𝑑−𝑐𝑐

� , 0� ,

Gaussian Membership Functions

A Gaussian membership function relies on two parameters: 𝑚𝑚 𝑎𝑎𝑎𝑎𝑎𝑎 𝜎𝜎. The parameter of ‘c’

presents center, and σ is the membership function’s width. The mathematical presentation of

Gaussian membership functions is in the following (Jang, Sun, & Mizutani, 1997).

𝑘𝑘𝑎𝑎𝑏𝑏𝐶𝐶𝐶𝐶𝑖𝑖𝑎𝑎𝑎𝑎 (𝑥𝑥; 𝑚𝑚,𝜎𝜎) = 𝑡𝑡−

12�𝑥𝑥−𝑐𝑐𝜎𝜎 �

Generalized Bell Membership Functions

A generalized bell membership function is known as bell membership function. The

membership function has three parameters, which are a, b, and c. The parameter ‘b’ can be

positive or negative. The variance of the parameter ‘b’ will have an impact on the shape of

membership functions. Generally, the parameter of b is positive, and the shape of the

membership function is in the bell shape, but if the parameter b is negative, the shape of the

membership function will be the upside-down bell shape, and the membership function also

refers to as the Cauchy membership function. The following equation displays a generalized

bell membership function (Jang, Sun, & Mizutani, 1997).

𝑏𝑏𝑡𝑡𝑖𝑖𝑖𝑖 (𝑥𝑥; 𝑎𝑎, 𝑏𝑏, 𝑚𝑚) = 1

1 + �𝑥𝑥 − 𝑚𝑚𝑎𝑎 �

2𝑏𝑏,

Sigmoidal Membership Functions

Two parameters (a and c) are in a sigmoidal membership function. The parameter ‘a’ controls

the slope at the crossover point 𝑥𝑥 = 𝑚𝑚. A sigmoidal membership function is shown in the

following (Jang, Sun, & Mizutani, 1997).

𝐶𝐶𝑖𝑖𝑘𝑘 (𝑥𝑥; 𝑎𝑎, 𝑚𝑚) = 1

1 + 𝑡𝑡𝑥𝑥𝑆𝑆[−𝑎𝑎(𝑥𝑥 − 𝑚𝑚)],

(a) (b)

(c) (d)

Figure B.1: Examples of Membership Functions: (a) triangular (x; 10, 50, 90), (b) trapezoid

(x; 10, 45, 60, 90), (c) gaussian (x; 20, 50), (d) bell (x; 20, 2.5, 50) (MATLAB, The

MathWorks Inc, 1994-2020)

Figure B.2: Typical Membership Functions of Linguistic Variable (MATLAB, The

MathWorks Inc, 1994-2020)

SUGENO FUZZY MODELS

The Sugeno type Fuzzy Inference System, known as the Takagi-Sugeno-Kang (TSK) Fuzzy

Inference System, was proposed by Takagi, Sugeno, and Kang (Takagi & Sugeno, 1985)

(Sugeno & Kang, 1988). Although the Sugeno type Fuzzy Inference System and the Mamdani

type Fuzzy Inference System are similar to each other, one of the most fundamental differences

between both systems is the crisp output generation from the fuzzy inputs. The output of the

Sugeno type Fuzzy Inference System can only be constant or linear. The weighted average is

used in the Sugeno type Fuzzy Inference System, which does not have any defuzzification

process. The advantages of the Sugeno type Fuzzy Inference System are displayed below

(Hamam & Georganas, 2008).

• Some algorithms can use to optimize the Sugeno type Fuzzy Inference System

automatically. One of the tools that can calibrate the weights of the Sugeno Fuzzy

Inference System output is MATLAB’s Adaptive Neuro-Fuzzy Inference System.

• Better processing time since the weighted average replace the time-consuming

defuzzification process.

• Computational efficiency and accuracy.

• If the noisy input data exists, the system is more robust.

• The fuzzy conditional statements’ consequents can have as many parameters for each

statement as input values allowing more degrees of freedom and more flexibility in the

design.

• Adequate for functional analysis because of the continuous structure of output function

(same inputs do not originate substantially different outputs).

The fuzzy conditional statements’ display for the Sugeno Fuzzy Inference System is “if 𝑥𝑥 is 𝐴𝐴

x is A and 𝑏𝑏 is 𝐵𝐵 then 𝑧𝑧 = 𝑖𝑖(𝑥𝑥, 𝑏𝑏)”, where the fuzzy sets in the antecedent are A and B, and a

crisp function in the consequent is 𝑧𝑧 = 𝑖𝑖(𝑥𝑥,𝑏𝑏) (Jang, Sun, & Mizutani, 1997).

There are different types of Sugeno Fuzzy Inference Systems, such as a first-order Sugeno

Fuzzy Model (Takagi & Sugeno, 1985) (Sugeno & Kang, 1988), a zero-order Sugeno Model

(Takagi & Sugeno, 1985) (Sugeno & Kang, 1988), and Tsukamoto Fuzzy Model (Tsukamoto,

1979).

DEFUZZIFICATION

Bisector of Area

Following equation represents the Bisector of Area, and the variables are 𝛼𝛼 = 𝑚𝑚𝑖𝑖𝑎𝑎{𝑧𝑧|𝑧𝑧 ∈ 𝑍𝑍}

and 𝛽𝛽 = 𝑚𝑚𝑎𝑎𝑥𝑥{𝑧𝑧|𝑧𝑧 ∈ 𝑍𝑍} (Jang, Sun, & Mizutani, 1997).

� 𝜇𝜇𝐴𝐴(𝑧𝑧)𝑎𝑎𝑧𝑧𝑧𝑧𝐵𝐵𝐵𝐵𝐵𝐵

𝛼𝛼 = � 𝜇𝜇𝐴𝐴(𝑧𝑧)𝑎𝑎𝑧𝑧

𝛽𝛽

𝑧𝑧𝐵𝐵𝐵𝐵𝐵𝐵,

Mean of Maximum

The mean of Maximum finds the average of all output values maximum membership function

degrees. The Mean of Maximum gives accurate results in a faster time frame. The symbolic

display of the mean of maximum is in following, and the variable is 𝑍𝑍′ = {𝑧𝑧|𝜇𝜇𝐴𝐴(𝑧𝑧) = 𝜇𝜇∗}

(Jang, Sun, & Mizutani, 1997).

𝑧𝑧𝑀𝑀𝐶𝐶𝑀𝑀 =

∫ 𝑧𝑧 𝑎𝑎𝑧𝑧𝑍𝑍′

∫ 𝑎𝑎𝑧𝑧𝑍𝑍′ ,

Smallest of Maximum

In terms of magnitude, the minimum value of the average of all output values maximum

membership function degrees is known as the Smallest of Maximum, and the Smallest of

Maximum is shown 𝑧𝑧𝑆𝑆𝐶𝐶𝑀𝑀 as symbolically (Jang, Sun, & Mizutani, 1997). The Smallest of

Maximum does not use so often according to other defuzzification methodologies.

Largest of Maximum

In terms of magnitude, the definition of the Largest of Maximum is that the maximum value of

the average of all output values maximum membership function degrees (Jang, Sun, &

Mizutani, 1997). The Largest of Maximum is not preferable defuzzification techniques among

the other methodologies. The symbolic presentation is 𝑧𝑧𝐿𝐿𝐶𝐶𝑀𝑀.

THE FUZZY LOGIC TOOLBOX

1. Fuzzy Inference System Editor: The editor identifies and modifies the Fuzzy Inference

System, and the editor provides adding or removing inputs and outputs.

Figure B.3: Fuzzy Inference System Editor (MATLAB, The MathWorks Inc, 1994-2020)

2. Membership Function Editor: The changes in membership functions are made in the

membership function editor for each input and output. Also, the parameters and shapes of

membership functions can be decided in the membership function editor.

Figure B.4: Membership Function Editor (MATLAB, The MathWorks Inc, 1994-2020)

3. Rule Editor: The definition of the fuzzy conditional statement is completed in the rule

editor.

Figure B.5: Rule Editor (MATLAB, The MathWorks Inc, 1994-2020)

4. Rule Viewer: The viewer is an interactive and innovative editor after completion of the

fuzzy conditional statements. The editor allows the users' understanding of the fuzzy

conditional statements responds, and the editor shows how each input change affects each

output.

Figure B.6: Rule Viewer (MATLAB, The MathWorks Inc, 1994-2020)

5. Surface Viewer: The interaction between the system’s any inputs and any outputs is

presented as a 2-Dimensional Graph and 3-Dimensional Graph in the surface viewer.

Figure B.7: 2-Dimensional Graph Surface Viewer (MATLAB, The MathWorks Inc, 1994-

Figure B.8: 3-Dimensional Graph Surface Viewer (MATLAB, The MathWorks Inc, 1994-

APPENDIX C: TESTING DATA

C.1. Randomized Produce Dataset

- Inputs

Personal Features Type II Diabetes Mellitus Tendency’s Inputs

Number of

Patients Gender Age

Family

History Nationality

Body Mass

1 Female 71 Just Dad WP 25

2 Male 50 Both Parents NAC 19.5

3 Male 49 Sibling EUR 19

4 Male 20 No History MENA 11

5 Male 94 Just Dad MENA 15

6 Male 26 No History EUR 11

7 Male 72 No History SEA 16

8 Male 36 Just Dad SCA 17

9 Male 70 Just Mom EUR 14

10 Female 86 Both Parents SEA 29

11 Female 50 No History AFR 28

12 Male 54 Sibling WP 25

13 Male 71 Sibling SCA 17

16 Female 74 Both Parents WP 37

17 Female 35 Just Mom WP 43

18 Female 28 Sibling AFR 25

20 Male 59 Just Mom NAC 49

21 Male 90 Both Parents WP 15

22 Female 36 Both Parents AFR 20

23 Female 23 Sibling EUR 25

24 Female 76 Both Parents SEA 36

25 Male 76 Just Dad AFR 26

26 Male 44 Both Parents EUR 15

27 Female 50 No History WP 31

29 Female 77 Just Mom MENA 23

31 Male 93 Both Parents NAC 47

32 Female 38 Sibling MENA 46

33 Male 61 Just Mom WP 32

34 Male 44 Just Dad SEA 10

35 Female 72 Just Dad MENA 37

36 Female 87 Just Dad SCA 38

38 Male 39 Sibling AFR 39

40 Male 25 Just Mom SEA 29

42 Female 71 Just Dad SEA 40

45 Male 61 No History AFR 33

48 Female 89 Sibling WP 14

49 Male 24 Sibling NAC 30

50 Male 70 Just Dad WP 49

52 Female 96 Just Mom MENA 5

53 Female 76 Both Parents SCA 23

54 Male 95 Both Parents MENA 24

56 Male 44 Sibling MENA 32

57 Female 97 No History SCA 12

58 Female 75 No History AFR 1

59 Female 40 Just Mom AFR 17

60 Female 99 No History NAC 34

64 Female 54 Just Dad SEA 27

65 Male 33 Just Dad EUR 30

67 Female 28 Both Parents NAC 49

68 Female 34 Just Mom SCA 26

69 Female 31 Both Parents EUR 18

70 Male 64 Both Parents SEA 24

72 Male 54 Both Parents EUR 8

73 Female 47 No History WP 45

74 Female 53 Both Parents MENA 3

75 Female 31 No History EUR 48

76 Female 21 No History NAC 28

77 Female 96 Just Dad NAC 25

79 Male 47 Both Parents MENA 39

80 Male 25 Just Dad WP 34

82 Male 71 Just Dad AFR 15

83 Male 57 No History SCA 30

85 Female 100 Both Parents NAC 40

87 Male 80 Sibling SEA 49

88 Female 72 Just Dad AFR 27

89 Female 84 Sibling SCA 31

91 Male 52 No History NAC 27

92 Female 71 Just Dad AFR 34

94 Male 59 Sibling AFR 7

95 Male 86 Just Mom SCA 20

97 Male 98 Sibling NAC 22

98 Male 30 Just Mom NAC 47

Biological Features Type II Diabetes Mellitus Tendency’s Inputs

Number of

Patients

Systolic

Pressure

Diastolic

Pressure

Cholesterol

Glucose

Pregnancy

Situation

1 278 81 89 73 Negative

2 62 127 440 36 Negative

3 268 91 20 67 Negative

4 132 71 230 36 Negative

5 125 196 123 31 Negative

6 196 19 291 17 Negative

7 44 98 93 5 Negative

8 48 190 83 56 Negative

9 61 17 191 74 Negative

10 50 76 234 60 Positive

11 147 105 64 56 Positive

12 109 43 376 109 Negative

13 135 9 356 122 Negative

14 136 74 498 118 Negative

15 37 80 438 118 Negative

16 214 188 163 122 Positive

17 240 74 121 120 Positive

18 131 114 453 118 Negative

19 225 33 46 110 Negative

20 205 68 357 106 Negative

21 98 199 297 110 Negative

22 15 10 277 109 Negative

23 292 21 151 220 Positive

24 203 130 448 245 Positive

25 232 121 497 545 Negative

26 116 132 181 682 Negative

27 203 24 140 644 Positive

28 72 181 214 785 Negative

29 53 85 218 202 Negative

30 220 146 332 324 Positive

31 166 53 358 442 Negative

32 230 182 486 397 Positive

33 212 115 330 639 Negative

34 213 140 449 110 Negative

35 160 164 99 85 Negative

36 71 6 312 131 Positive

37 269 72 2 101 Positive

38 160 21 193 458 Negative

39 253 83 257 66 Positive

40 218 129 482 296 Negative

41 55 161 192 307 Negative

42 101 75 156 39 Positive

43 245 73 223 105 Positive

44 64 29 69 111 Negative

45 183 123 88 541 Negative

46 209 6 388 396 Positive

47 35 126 216 472 Negative

48 157 6 213 75 Negative

49 36 10 158 89 Negative

50 286 134 80 120 Negative

51 39 4 192 444 Negative

52 101 129 454 111 Negative

53 62 185 469 522 Positive

54 135 173 95 141 Negative

55 202 92 107 85 Positive

56 217 23 9 80 Negative

57 224 13 474 422 Positive

58 71 82 179 410 Negative

59 173 8 497 108 Positive

60 299 20 240 766 Negative

61 178 122 258 556 Negative

62 158 15 323 64 Positive

63 255 140 174 269 Negative

64 24 166 32 619 Positive

65 151 4 371 93 Negative

66 234 168 163 696 Positive

67 245 169 4 188 Negative

68 31 173 50 113 Negative

69 86 43 426 741 Positive

70 292 128 310 111 Negative

71 125 45 342 692 Positive

72 134 19 365 182 Negative

73 277 180 128 55 Negative

74 120 17 34 196 Positive

75 290 125 131 135 Positive

76 205 193 46 117 Negative

77 172 130 234 204 Positive

78 279 171 122 280 Negative

79 294 4 471 119 Negative

80 240 7 153 58 Negative

81 42 185 312 288 Negative

82 216 177 94 91 Negative

83 8 88 109 37 Negative

84 169 153 404 50 Positive

85 110 144 485 737 Negative

86 234 131 488 264 Negative

87 76 117 82 104 Negative

88 96 26 98 65 Negative

89 188 48 88 296 Negative

90 50 29 95 382 Negative

91 34 45 318 123 Negative

92 236 191 270 109 Negative

93 44 124 479 326 Negative

94 216 125 313 279 Negative

95 104 48 142 496 Negative

96 174 170 317 62 Positive

97 76 36 459 609 Negative

98 230 145 194 93 Negative

99 229 50 184 625 Negative

100 280 80 25 496 Negative

Lifestyle Habits Type II Diabetes Mellitus Tendency’s Inputs

Number of

Patients Dietary Factors

Physical Activity

Smoking

Alcohol

Consumption

1 Healthier Low Activity Active User No Usage

2 Unhealthier Sedentary Former Low Usage

3 Unhealthier Very Active Active User Regular Drinker

4 Healthier Active Active User Addicted

5 Partial Healthier Very Active Passive Usage Addicted

6 Unhealthier Low Activity Active User Regular Drinker

7 Partial Healthier Sedentary Passive Usage Low Usage

8 Partial Healthier Very Active Active User Addicted

9 Partial Healthier Low Activity No Usage Regular Drinker

10 Partial Healthier Very Active Active User Addicted

11 Partial Healthier Active Former Addicted

12 Partial Healthier Active Active User Low Usage

13 Unhealthier Low Activity Passive Usage Addicted

15 Unhealthier Very Active Passive Usage Low Usage

16 Partial Healthier Low Activity Former Addicted

17 Healthier Sedentary Former Regular Drinker

18 Healthier Very Active Passive Usage Low Usage

19 Healthier Low Activity No Usage Regular Drinker

20 Unhealthier Very Active Former Regular Drinker

21 Healthier Sedentary Passive Usage No Usage

23 Partial Healthier Low Activity Active User Regular Drinker

24 Partial Healthier Active Active User Low Usage

25 Unhealthier Active No Usage No Usage

26 Unhealthier Very Active No Usage Low Usage

27 Healthier Active No Usage Regular Drinker

28 Healthier Low Activity Former No Usage

29 Partial Healthier Sedentary Active User Addicted

30 Partial Healthier Low Activity No Usage Regular Drinker

31 Unhealthier Active Former Regular Drinker

32 Unhealthier Active No Usage Addicted

34 Healthier Sedentary Active User Addicted

35 Partial Healthier Active No Usage Addicted

36 Healthier Low Activity No Usage Regular Drinker

37 Unhealthier Very Active Former Regular Drinker

38 Healthier Very Active Former No Usage

39 Healthier Sedentary No Usage Addicted

40 Unhealthier Very Active No Usage Regular Drinker

41 Partial Healthier Sedentary Former Addicted

42 Healthier Active No Usage Regular Drinker

43 Healthier Active Former Addicted

44 Unhealthier Sedentary Active User No Usage

45 Healthier Active Passive Usage Low Usage

46 Healthier Very Active Passive Usage Addicted

47 Unhealthier Active Former No Usage

49 Healthier Low Activity Former Regular Drinker

50 Partial Healthier Sedentary Former No Usage

51 Partial Healthier Low Activity Passive Usage Regular Drinker

52 Healthier Sedentary Active User Regular Drinker

53 Unhealthier Low Activity Passive Usage No Usage

54 Healthier Low Activity Passive Usage Regular Drinker

55 Healthier Low Activity Active User Addicted

56 Unhealthier Low Activity Passive Usage Low Usage

57 Healthier Active Former Addicted

58 Partial Healthier Sedentary Active User Regular Drinker

59 Healthier Very Active Former Regular Drinker

61 Partial Healthier Active Former Addicted

62 Unhealthier Active Former No Usage

63 Partial Healthier Sedentary Former Regular Drinker

64 Healthier Active Active User Addicted

65 Partial Healthier Low Activity No Usage Low Usage

67 Unhealthier Low Activity Former Addicted

68 Partial Healthier Sedentary Passive Usage Regular Drinker

69 Unhealthier Low Activity Active User Regular Drinker

70 Healthier Sedentary Former Low Usage

71 Healthier Low Activity No Usage Low Usage

72 Healthier Very Active Passive Usage Regular Drinker

73 Unhealthier Very Active Passive Usage Low Usage

74 Partial Healthier Very Active No Usage Regular Drinker

75 Partial Healthier Very Active Former Addicted

76 Healthier Sedentary Former No Usage

77 Partial Healthier Very Active Active User No Usage

78 Healthier Very Active Active User Low Usage

79 Unhealthier Low Activity Passive Usage No Usage

81 Unhealthier Active Passive Usage Regular Drinker

82 Healthier Low Activity Active User Addicted

83 Healthier Low Activity Former Addicted

84 Healthier Low Activity No Usage Low Usage

85 Unhealthier Low Activity No Usage Low Usage

86 Partial Healthier Low Activity Active User No Usage

88 Partial Healthier Very Active Former No Usage

89 Unhealthier Very Active No Usage Regular Drinker

90 Unhealthier Sedentary Passive Usage Low Usage

91 Healthier Low Activity Passive Usage Addicted

92 Partial Healthier Low Activity No Usage Addicted

93 Healthier Very Active Active User Regular Drinker

94 Partial Healthier Active Active User No Usage

95 Healthier Active Active User Low Usage

96 Partial Healthier Low Activity Passive Usage No Usage

97 Partial Healthier Sedentary Former Addicted

98 Partial Healthier Sedentary Active User Regular Drinker

99 Healthier Very Active Active User Low Usage

100 Partial Healthier Low Activity Active User Addicted

- Outputs

Number of Patients Doctor’s Opinion The Output of the

Proposed System

1 Healthier Healthier

10 Healthier Prediabetes

12 Prediabetes Type2

13 Prediabetes Prediabetes

23 Type 2 Type2

24 Type 2 Type2

25 Type 2 Type2

26 Type 2 Prediabetes

27 Type 2 Type2

28 Type 2 Type2

29 Type 2 Type2

30 Type 2 Type2

31 Type 2 Type2

32 Type 2 Type2

33 Type 2 Type2

36 Type 2 Type2

38 Type 2 Type2

40 Type 2 Type2

41 Type 2 Type2

43 Type 2 Type2

45 Type 2 Type2

46 Type 2 Type2

47 Type 2 Type2

51 Type 2 Type2

53 Type 2 Type2

54 Type 2 Type2

57 Type 2 Type2

58 Type 2 Type2

60 Type 2 Type2

61 Type 2 Type2

63 Type 2 Type2

64 Type 2 Type2

66 Type 2 Type2

67 Type 2 Type2

69 Type 2 Type2

71 Type 2 Type2

72 Type 2 Type2

74 Type 2 Type2

75 Type 2 Type2

77 Type 2 Type2

78 Type 2 Type2

81 Type 2 Type2

85 Type 2 Type2

86 Type 2 Type2

89 Type 2 Type2

90 Type 2 Type2

93 Type 2 Type2

94 Type 2 Type2

95 Type 2 Type2

97 Type 2 Type2

99 Type 2 Type2

100 Type 2 Type2

C.2. Pima Indian Diabetes Dataset

- Inputs

Number of

Patients Gender Age

Family

History Nationality

Body Mass

1 Female 50 Unknown NAC 33.6

6 Female 26 Unknown NAC 31

Number of

Patients

Systolic

Pressure

Diastolic

Pressure

Cholesterol

Glucose

Pregnancy

Situation

1 Unknown 72 Unknown 148 Negative

32 Unknown Unknown 115 Negative

Number of

Patients

Dietary

Factors

Physical Activity

Smoking

Alcohol

Consumption

1 Unknown Unknown Unknown Unknown

- Outputs

Number of Patients Doctor’s Opinion The Output of the Proposed

System

1 Diabetes Possible Type 2

2 Non-Diabetes Healthier

8 Non-Diabetes Possible Prediabetes

9 Non-Diabetes Possible Type 2

14 Diabetes Possible Pre-Diabetes

34 Diabetes Possible Prediabetes

36 Diabetes Possible Prediabetes

38 Diabetes Healthier

48 Diabetes Healthier

65 Type 2 Type 2

69 Type 2 Type 2

76 Healthier Type 2

81 Type 2 Type 2

82 Healthier Type 2

86 Healthier Type 2

88 Healthier Type 2

90 Type 2 Healthier

91 Type 2 Type 2

92 Type 2 Type 2

95 Type 2 Type 2

96 Type 2 Type 2

C.3. BioStatistics Diabetes Dataset

- Inputs

Number of

Patients Gender Age

Family

History Nationality

Body Mass

1 Female 51 Unknown EUR 33.7

3 Female 91 Unknown EUR 24

6 Male 40 Unknown EUR 29.8

28 Male 47 Unknown EUR 36

Number of

Patients

Systolic

Pressure

Diastolic

Pressure

Cholesterol

Glucose

Pregnancy

Situation

1 158 98 443 364 Negative

2 160 60 173 81 Negative

3 170 82 232 194 Negative

4 126 80 289 223 Negative

5 165 110 193 136 Negative

6 138 94 171 85 Negative

7 110 68 128 266 Negative

8 140 65 148 130 Negative

9 130 60 213 76 Negative

10 160 96 182 180 Negative

11 124 64 173 80 Negative

12 176 86 182 85 Negative

13 170 88 265 399 Negative

14 140 95 232 87 Negative

15 118 59 203 77 Negative

16 112 68 165 81 Negative

17 190 92 228 86 Negative

18 110 50 78 86 Negative

19 138 80 249 175 Negative

20 132 86 248 91 Negative

21 161 112 195 92 Negative

22 100 90 230 83 Negative

23 160 80 177 92 Negative

24 102 68 215 96 Negative

25 130 80 238 82 Negative

26 178 90 196 276 Negative

27 148 88 186 140 Negative

28 137 100 234 87 Negative

29 136 83 203 319 Negative

30 158 88 281 113 Negative

31 120 80 189 93 Negative

32 125 64 229 93 Negative

33 121 62 228 71 Negative

34 138 79 159 97 Negative

35 151 85 249 216 Negative

36 103 64 170 100 Negative

37 119 72 174 99 Negative

38 110 90 204 92 Negative

39 117 68 203 71 Negative

40 112 72 241 91 Negative

41 142 102 245 177 Negative

42 160 92 143 91 Negative

43 160 80 224 245 Negative

44 111 65 168 73 Negative

45 136 96 184 70 Negative

46 136 84 199 109 Negative

47 136 90 158 77 Negative

48 115 68 209 234 Negative

49 130 90 214 65 Negative

50 110 90 293 85 Negative

51 150 90 227 117 Negative

52 170 90 292 179 Negative

53 130 73 218 80 Negative

54 132 90 244 87 Negative

55 158 104 283 74 Negative

56 138 82 186 74 Negative

57 160 96 273 61 Negative

58 160 68 215 233 Negative

59 131 88 194 57 Negative

60 141 99 231 105 Negative

61 160 68 215 233 Negative

62 124 78 185 92 Negative

63 112 92 132 68 Negative

64 110 80 175 64 Negative

65 110 76 179 95 Negative

66 170 92 228 115 Negative

67 130 66 181 169 Negative

68 180 110 160 86 Negative

69 122 70 188 91 Negative

70 130 88 168 99 Negative

71 132 72 318 140 Negative

72 141 79 192 93 Negative

73 111 62 209 80 Negative

74 140 75 129 129 Negative

75 136 83 160 140 Negative

76 150 98 160 169 Negative

77 162 78 211 153 Negative

78 110 68 262 94 Negative

79 145 95 201 91 Negative

80 104 64 263 85 Negative

81 146 92 219 217 Negative

82 130 90 191 110 Negative

83 125 72 171 69 Negative

84 125 82 219 103 Negative

85 140 86 347 135 Negative

86 126 90 269 73 Negative

87 108 78 164 83 Negative

88 140 86 181 228 Negative

89 135 88 190 113 Negative

90 179 89 255 114 Negative

91 139 90 218 93 Negative

92 120 86 223 114 Negative

93 151 87 254 326 Negative

94 150 80 236 115 Negative

95 110 72 176 82 Negative

96 138 89 158 113 Negative

97 140 98 181 69 Negative

98 110 90 151 79 Negative

99 150 106 190 87 Negative

100 146 76 271 84 Negative

Number of

Patients

Dietary

Factors

Physical Activity

Smoking

Alcohol

Consumption

- Outputs

Number of Patients Doctor’s Opinion The Output of the Proposed

System

1 Diabetes Type 2

3 Diabetes Prediabetes

4 Diabetes Type 2

7 Diabetes Type 2

8 Prediabetes Type 2

10 Diabetes Type 2

19 Diabetes Type 2

26 Diabetes Type 2

29 Diabetes Type 2

35 Diabetes Type 2

37 Non-Diabetes Prediabetes

41 Diabetes Type 2

43 Diabetes Diabetes

48 Diabetes Type 2

58 Diabetes Type 2

60 Non-Diabetes Prediabetes

61 Type 2 Type 2

67 Type 2 Type 2

76 Type 2 Type 2

77 Type 2 Type 2

81 Type 2 Type 2

88 Type 2 Type 2

93 Type 2 Type 2

94 Prediabetes Healthier

APPENDIX D: FUZZY INFERENCE SYSTEM

D.1. Personal Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

- The System

- Inputs’ Membership Functions

- Output’s Membership Function & Surface Graph

D.2 Biological Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

- The System

- Inputs’ Membership Function

D.3 Lifestyle Habits Type II Diabetes Mellitus Tendency Fuzzy Inference System

- The System

D.4 Primary Diagnosis Type II Diabetes Mellitus Tendency Fuzzy Inference System

- The System

D.5 Secondary Diagnosis Type II Diabetes Mellitus Tendency Fuzzy Inference System

- The System

APPENDIX E: FUZZY LOGIC CODE

E.1. Personal Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

[SYSTEM] NAME='PERSONALFEATURESDIABETESTENDENCY' TYPE='MAMDANI' VERSION=2.0 NUMINPUTS=5 NUMOUTPUTS=1 NUMRULES=1400 ANDMETHOD='MIN' ORMETHOD='MAX' IMPMETHOD='MIN' AGGMETHOD='MAX' DEFUZZMETHOD='CENTROID' [INPUT1] NAME='GENDER' RANGE=[0 100] NUMMFS=2 MF1='FEMALE':'TRIMF',[-100 0 103.5] MF2='MALE':'TRIMF',[0 100 200] [INPUT2] NAME='AGE' RANGE=[19 100] NUMMFS=5 MF1='20-29YEARSOLD':'TRIMF',[19 24.06 29.13] MF2='30-39YEARSOLD':'TRIMF',[28.11 34.09 39.25] MF3='40-49YEARSOLD':'TRIMF',[38.24 44.67 49.38] MF4='50-59YEARSOLD':'TRIMF',[48.36 54.34 59.5] MF5='60-OLDERYEARSOLD':'TRIMF',[58.49 79.75 100] [INPUT3] NAME='FAMILYHISTORY' RANGE=[0 100] NUMMFS=5 MF1='JUSTMOTHER':'TRIMF',[0 25 50] MF2='JUSTFATHER':'TRIMF',[25 50 75] MF3='BOTHPARENTS':'TRIMF',[50 75 100] MF4='JUSTASIBLING':'TRIMF',[75 100 125] MF5='NOHISTORY':'TRIMF',[0 0 25] [INPUT4] NAME='NATIONALITY' RANGE=[0 100] NUMMFS=7 MF1='NAMERICA&CARIBBEAN':'TRIMF',[-16.67 0 16.67] MF2='MEAST&NAFRICA':'TRIMF',[0.09251 16.76 33.42] MF3='EUROPE':'TRIMF',[16.58 33.24 49.91] MF4='WESTERNPACIFIC':'TRIMF',[33.5945502645503 50.2645502645503 66.9345502645503] MF5='SEASIA':'TRIMF',[50 66.67 83.33] MF6='AFRICA':'TRIMF',[66.21 82.91 99.21] MF7='S&CAMERICA':'TRIMF',[83.33 100 116.7]

[INPUT5] NAME='BODYMASSINDEX' RANGE=[-1 50] NUMMFS=4 MF1='UNDERWEIGHT':'TRIMF',[-1 8.18 17.87] MF2='NORMAL-WEIGHT':'TRIMF',[17.77 20.83 24.5] MF3='OVERWEIGHT':'TRIMF',[24.4 26.76 29.6] MF4='OBESE':'TRIMF',[29.5 39.8 50] [OUTPUT1] NAME='PERSONALFEATURESTENDENCY' RANGE=[0 100] NUMMFS=3 MF1='LOWRISK':'TRIMF',[-40 0 40] MF2='MEDIUMRISK':'TRIMF',[9.907 49.91 89.91] MF3='HIGHRISK':'TRIMF',[60 100 140] [RULES] 1 1 1 1 1, 1 (1) : 1 1 1 1 1 2, 1 (1) : 1 1 1 1 1 3, 2 (1) : 1 1 1 1 1 4, 3 (1) : 1 1 1 1 2 1, 1 (1) : 1 1 1 1 2 2, 1 (1) : 1 1 1 1 2 3, 2 (1) : 1 1 1 1 2 4, 2 (1) : 1 1 1 1 3 1, 1 (1) : 1 1 1 1 3 2, 1 (1) : 1 1 1 1 3 3, 2 (1) : 1 1 1 1 3 4, 2 (1) : 1 1 1 1 4 1, 1 (1) : 1 1 1 1 4 2, 2 (1) : 1 1 1 1 4 3, 2 (1) : 1 1 1 1 4 4, 3 (1) : 1 1 1 1 5 1, 1 (1) : 1 1 1 1 5 2, 2 (1) : 1 1 1 1 5 3, 2 (1) : 1 1 1 1 5 4, 3 (1) : 1 1 1 1 6 1, 1 (1) : 1 1 1 1 6 2, 1 (1) : 1 1 1 1 6 3, 1 (1) : 1 1 1 1 6 4, 1 (1) : 1 1 1 1 7 1, 1 (1) : 1 1 1 1 7 2, 1 (1) : 1 1 1 1 7 3, 1 (1) : 1 1 1 1 7 4, 1 (1) : 1 1 1 2 1 1, 1 (1) : 1 1 1 2 1 2, 1 (1) : 1 1 1 2 1 3, 2 (1) : 1 1 1 2 1 4, 2 (1) : 1 1 1 2 2 1, 1 (1) : 1 1 1 2 2 2, 1 (1) : 1 1 1 2 2 3, 2 (1) : 1 1 1 2 2 4, 2 (1) : 1 1 1 2 3 1, 1 (1) : 1 1 1 2 3 2, 1 (1) : 1 1 1 2 3 3, 2 (1) : 1 1 1 2 3 4, 2 (1) : 1 1 1 2 4 1, 2 (1) : 1

1 1 2 4 2, 2 (1) : 1 1 1 2 4 3, 3 (1) : 1 1 1 2 4 4, 3 (1) : 1 1 1 2 5 1, 2 (1) : 1 1 1 2 5 2, 2 (1) : 1 1 1 2 5 3, 3 (1) : 1 1 1 2 5 4, 3 (1) : 1 1 1 2 6 1, 1 (1) : 1 1 1 2 6 2, 1 (1) : 1 1 1 2 6 3, 1 (1) : 1 1 1 2 6 4, 2 (1) : 1 1 1 2 7 1, 1 (1) : 1 1 1 2 7 2, 1 (1) : 1 1 1 2 7 3, 2 (1) : 1 1 1 2 7 4, 2 (1) : 1 1 1 3 1 1, 2 (1) : 1 1 1 3 1 2, 2 (1) : 1 1 1 3 1 3, 3 (1) : 1 1 1 3 1 4, 3 (1) : 1 1 1 3 2 1, 1 (1) : 1 1 1 3 2 2, 1 (1) : 1 1 1 3 2 3, 2 (1) : 1 1 1 3 2 4, 2 (1) : 1 1 1 3 3 1, 1 (1) : 1 1 1 3 3 2, 1 (1) : 1 1 1 3 3 3, 2 (1) : 1 1 1 3 3 4, 2 (1) : 1 1 1 3 4 1, 2 (1) : 1 1 1 3 4 2, 2 (1) : 1 1 1 3 4 3, 3 (1) : 1 1 1 3 4 4, 3 (1) : 1 1 1 3 5 1, 2 (1) : 1 1 1 3 5 2, 2 (1) : 1 1 1 3 5 3, 3 (1) : 1 1 1 3 5 4, 3 (1) : 1 1 1 3 6 1, 1 (1) : 1 1 1 3 6 2, 1 (1) : 1 1 1 3 6 3, 2 (1) : 1 1 1 3 6 4, 2 (1) : 1 1 1 3 7 1, 1 (1) : 1 1 1 3 7 2, 1 (1) : 1 1 1 3 7 3, 2 (1) : 1 1 1 3 7 4, 2 (1) : 1 1 1 4 1 1, 1 (1) : 1 1 1 4 1 2, 1 (1) : 1 1 1 4 1 3, 2 (1) : 1 1 1 4 1 4, 2 (1) : 1 1 1 4 2 1, 1 (1) : 1 1 1 4 2 2, 1 (1) : 1 1 1 4 2 3, 2 (1) : 1 1 1 4 2 4, 2 (1) : 1 1 1 4 3 1, 1 (1) : 1 1 1 4 3 2, 1 (1) : 1 1 1 4 3 3, 2 (1) : 1 1 1 4 3 4, 2 (1) : 1 1 1 4 4 1, 1 (1) : 1 1 1 4 4 2, 2 (1) : 1 1 1 4 4 3, 2 (1) : 1 1 1 4 4 4, 3 (1) : 1 1 1 4 5 1, 1 (1) : 1

1 1 4 5 2, 2 (1) : 1 1 1 4 5 3, 2 (1) : 1 1 1 4 5 4, 3 (1) : 1 1 1 4 6 1, 1 (1) : 1 1 1 4 6 2, 1 (1) : 1 1 1 4 6 3, 2 (1) : 1 1 1 4 6 4, 2 (1) : 1 1 1 4 7 1, 1 (1) : 1 1 1 4 7 2, 1 (1) : 1 1 1 4 7 3, 2 (1) : 1 1 1 4 7 4, 2 (1) : 1 1 2 1 1 1, 1 (1) : 1 1 2 1 1 2, 1 (1) : 1 1 2 1 1 3, 2 (1) : 1 1 2 1 1 4, 3 (1) : 1 1 2 1 2 1, 1 (1) : 1 1 2 1 2 2, 1 (1) : 1 1 2 1 2 3, 2 (1) : 1 1 2 1 2 4, 3 (1) : 1 1 2 1 3 1, 1 (1) : 1 1 2 1 3 2, 1 (1) : 1 1 2 1 3 3, 2 (1) : 1 1 2 1 3 4, 2 (1) : 1 1 2 1 4 1, 2 (1) : 1 1 2 1 4 2, 2 (1) : 1 1 2 1 4 3, 3 (1) : 1 1 2 1 4 4, 3 (1) : 1 1 2 1 5 1, 2 (1) : 1 1 2 1 5 2, 2 (1) : 1 1 2 1 5 3, 3 (1) : 1 1 2 1 5 4, 3 (1) : 1 1 2 1 6 1, 1 (1) : 1 1 2 1 6 2, 1 (1) : 1 1 2 1 6 3, 2 (1) : 1 1 2 1 6 4, 2 (1) : 1 1 2 1 7 1, 1 (1) : 1 1 2 1 7 2, 1 (1) : 1 1 2 1 7 3, 2 (1) : 1 1 2 1 7 4, 2 (1) : 1 1 2 2 1 1, 2 (1) : 1 1 2 2 1 2, 2 (1) : 1 1 2 2 1 3, 3 (1) : 1 1 2 2 1 4, 3 (1) : 1 1 2 2 2 1, 1 (1) : 1 1 2 2 2 2, 1 (1) : 1 1 2 2 2 3, 2 (1) : 1 1 2 2 2 4, 2 (1) : 1 1 2 2 3 1, 1 (1) : 1 1 2 2 3 2, 1 (1) : 1 1 2 2 3 3, 2 (1) : 1 1 2 2 3 4, 2 (1) : 1 1 2 2 4 1, 2 (1) : 1 1 2 2 4 2, 2 (1) : 1 1 2 2 4 3, 3 (1) : 1 1 2 2 4 4, 3 (1) : 1 1 2 2 5 1, 2 (1) : 1 1 2 2 5 2, 2 (1) : 1 1 2 2 5 3, 3 (1) : 1 1 2 2 5 4, 3 (1) : 1 1 2 2 6 1, 1 (1) : 1

1 2 2 6 2, 1 (1) : 1 1 2 2 6 3, 2 (1) : 1 1 2 2 6 4, 2 (1) : 1 1 2 2 7 1, 1 (1) : 1 1 2 2 7 2, 1 (1) : 1 1 2 2 7 3, 2 (1) : 1 1 2 2 7 4, 2 (1) : 1 1 2 3 1 1, 2 (1) : 1 1 2 3 1 2, 2 (1) : 1 1 2 3 1 3, 3 (1) : 1 1 2 3 1 4, 3 (1) : 1 1 2 3 2 1, 1 (1) : 1 1 2 3 2 2, 2 (1) : 1 1 2 3 2 3, 2 (1) : 1 1 2 3 2 4, 3 (1) : 1 1 2 3 3 1, 1 (1) : 1 1 2 3 3 2, 2 (1) : 1 1 2 3 3 3, 2 (1) : 1 1 2 3 3 4, 3 (1) : 1 1 2 3 4 1, 2 (1) : 1 1 2 3 4 2, 3 (1) : 1 1 2 3 4 3, 3 (1) : 1 1 2 3 4 4, 3 (1) : 1 1 2 3 5 1, 2 (1) : 1 1 2 3 5 2, 3 (1) : 1 1 2 3 5 3, 3 (1) : 1 1 2 3 5 4, 3 (1) : 1 1 2 3 6 1, 1 (1) : 1 1 2 3 6 2, 2 (1) : 1 1 2 3 6 3, 2 (1) : 1 1 2 3 6 4, 3 (1) : 1 1 2 3 7 1, 1 (1) : 1 1 2 3 7 2, 2 (1) : 1 1 2 3 7 3, 2 (1) : 1 1 2 3 7 4, 3 (1) : 1 1 2 4 1 1, 1 (1) : 1 1 2 4 1 2, 1 (1) : 1 1 2 4 1 3, 2 (1) : 1 1 2 4 1 4, 2 (1) : 1 1 2 4 2 1, 1 (1) : 1 1 2 4 2 2, 1 (1) : 1 1 2 4 2 3, 2 (1) : 1 1 2 4 2 4, 2 (1) : 1 1 2 4 3 1, 1 (1) : 1 1 2 4 3 2, 1 (1) : 1 1 2 4 3 3, 2 (1) : 1 1 2 4 3 4, 2 (1) : 1 1 2 4 4 1, 1 (1) : 1 1 2 4 4 2, 1 (1) : 1 1 2 4 4 3, 3 (1) : 1 1 2 4 4 4, 3 (1) : 1 1 2 4 5 1, 1 (1) : 1 1 2 4 5 2, 1 (1) : 1 1 2 4 5 3, 2 (1) : 1 1 2 4 5 4, 3 (1) : 1 1 2 4 6 1, 1 (1) : 1 1 2 4 6 2, 1 (1) : 1 1 2 4 6 3, 2 (1) : 1 1 2 4 6 4, 2 (1) : 1 1 2 4 7 1, 1 (1) : 1

1 2 4 7 2, 1 (1) : 1 1 2 4 7 3, 2 (1) : 1 1 2 4 7 4, 2 (1) : 1 1 3 1 1 1, 1 (1) : 1 1 3 1 1 2, 2 (1) : 1 1 3 1 1 3, 3 (1) : 1 1 3 1 1 4, 3 (1) : 1 1 3 1 2 1, 1 (1) : 1 1 3 1 2 2, 2 (1) : 1 1 3 1 2 3, 2 (1) : 1 1 3 1 2 4, 3 (1) : 1 1 3 1 3 1, 1 (1) : 1 1 3 1 3 2, 2 (1) : 1 1 3 1 3 3, 2 (1) : 1 1 3 1 3 4, 3 (1) : 1 1 3 1 4 1, 2 (1) : 1 1 3 1 4 2, 2 (1) : 1 1 3 1 4 3, 3 (1) : 1 1 3 1 4 4, 3 (1) : 1 1 3 1 5 1, 2 (1) : 1 1 3 1 5 2, 2 (1) : 1 1 3 1 5 3, 3 (1) : 1 1 3 1 5 4, 3 (1) : 1 1 3 1 6 1, 1 (1) : 1 1 3 1 6 2, 2 (1) : 1 1 3 1 6 3, 2 (1) : 1 1 3 1 6 4, 3 (1) : 1 1 3 1 7 1, 1 (1) : 1 1 3 1 7 2, 2 (1) : 1 1 3 1 7 3, 2 (1) : 1 1 3 1 7 4, 3 (1) : 1 1 3 2 1 1, 2 (1) : 1 1 3 2 1 2, 2 (1) : 1 1 3 2 1 3, 3 (1) : 1 1 3 2 1 4, 3 (1) : 1 1 3 2 2 1, 2 (1) : 1 1 3 2 2 2, 2 (1) : 1 1 3 2 2 3, 3 (1) : 1 1 3 2 2 4, 3 (1) : 1 1 3 2 3 1, 1 (1) : 1 1 3 2 3 2, 1 (1) : 1 1 3 2 3 3, 2 (1) : 1 1 3 2 3 4, 3 (1) : 1 1 3 2 4 1, 2 (1) : 1 1 3 2 4 2, 2 (1) : 1 1 3 2 4 3, 3 (1) : 1 1 3 2 4 4, 3 (1) : 1 1 3 2 5 1, 2 (1) : 1 1 3 2 5 2, 2 (1) : 1 1 3 2 5 3, 3 (1) : 1 1 3 2 5 4, 3 (1) : 1 1 3 2 6 1, 1 (1) : 1 1 3 2 6 2, 1 (1) : 1 1 3 2 6 3, 2 (1) : 1 1 3 2 6 4, 3 (1) : 1 1 3 2 7 1, 1 (1) : 1 1 3 2 7 2, 1 (1) : 1 1 3 2 7 3, 2 (1) : 1 1 3 2 7 4, 3 (1) : 1 1 3 3 1 1, 2 (1) : 1

1 3 3 1 2, 3 (1) : 1 1 3 3 1 3, 3 (1) : 1 1 3 3 1 4, 3 (1) : 1 1 3 3 2 1, 1 (1) : 1 1 3 3 2 2, 2 (1) : 1 1 3 3 2 3, 3 (1) : 1 1 3 3 2 4, 3 (1) : 1 1 3 3 3 1, 1 (1) : 1 1 3 3 3 2, 2 (1) : 1 1 3 3 3 3, 3 (1) : 1 1 3 3 3 4, 3 (1) : 1 1 3 3 4 1, 2 (1) : 1 1 3 3 4 2, 2 (1) : 1 1 3 3 4 3, 3 (1) : 1 1 3 3 4 4, 3 (1) : 1 1 3 3 5 1, 2 (1) : 1 1 3 3 5 2, 2 (1) : 1 1 3 3 5 3, 3 (1) : 1 1 3 3 5 4, 3 (1) : 1 1 3 3 6 1, 1 (1) : 1 1 3 3 6 2, 1 (1) : 1 1 3 3 6 3, 2 (1) : 1 1 3 3 6 4, 3 (1) : 1 1 3 3 7 1, 1 (1) : 1 1 3 3 7 2, 1 (1) : 1 1 3 3 7 3, 2 (1) : 1 1 3 3 7 4, 3 (1) : 1 1 3 4 1 1, 1 (1) : 1 1 3 4 1 2, 2 (1) : 1 1 3 4 1 3, 3 (1) : 1 1 3 4 1 4, 3 (1) : 1 1 3 4 2 1, 1 (1) : 1 1 3 4 2 2, 2 (1) : 1 1 3 4 2 3, 3 (1) : 1 1 3 4 2 4, 3 (1) : 1 1 3 4 3 1, 1 (1) : 1 1 3 4 3 2, 2 (1) : 1 1 3 4 3 3, 2 (1) : 1 1 3 4 3 4, 3 (1) : 1 1 3 4 4 1, 1 (1) : 1 1 3 4 4 2, 2 (1) : 1 1 3 4 4 3, 3 (1) : 1 1 3 4 4 4, 3 (1) : 1 1 3 4 5 1, 1 (1) : 1 1 3 4 5 2, 2 (1) : 1 1 3 4 5 3, 3 (1) : 1 1 3 4 5 4, 3 (1) : 1 1 3 4 6 1, 1 (1) : 1 1 3 4 6 2, 2 (1) : 1 1 3 4 6 3, 2 (1) : 1 1 3 4 6 4, 3 (1) : 1 1 3 4 7 1, 1 (1) : 1 1 3 4 7 2, 2 (1) : 1 1 3 4 7 3, 2 (1) : 1 1 3 4 7 4, 3 (1) : 1 1 4 1 1 1, 1 (1) : 1 1 4 1 1 2, 2 (1) : 1 1 4 1 1 3, 3 (1) : 1 1 4 1 1 4, 3 (1) : 1 1 4 1 2 1, 1 (1) : 1

1 4 1 2 2, 2 (1) : 1 1 4 1 2 3, 3 (1) : 1 1 4 1 2 4, 3 (1) : 1 1 4 1 3 1, 1 (1) : 1 1 4 1 3 2, 2 (1) : 1 1 4 1 3 3, 2 (1) : 1 1 4 1 3 4, 3 (1) : 1 1 4 1 4 1, 1 (1) : 1 1 4 1 4 2, 2 (1) : 1 1 4 1 4 3, 3 (1) : 1 1 4 1 4 4, 3 (1) : 1 1 4 1 5 1, 1 (1) : 1 1 4 1 5 2, 2 (1) : 1 1 4 1 5 3, 3 (1) : 1 1 4 1 5 4, 3 (1) : 1 1 4 1 6 1, 1 (1) : 1 1 4 1 6 2, 2 (1) : 1 1 4 1 6 3, 2 (1) : 1 1 4 1 6 4, 3 (1) : 1 1 4 1 7 1, 1 (1) : 1 1 4 1 7 2, 2 (1) : 1 1 4 1 7 3, 2 (1) : 1 1 4 1 7 4, 3 (1) : 1 1 4 2 1 1, 1 (1) : 1 1 4 2 1 2, 2 (1) : 1 1 4 2 1 3, 3 (1) : 1 1 4 2 1 4, 3 (1) : 1 1 4 2 2 1, 1 (1) : 1 1 4 2 2 2, 2 (1) : 1 1 4 2 2 3, 3 (1) : 1 1 4 2 2 4, 3 (1) : 1 1 4 2 3 1, 1 (1) : 1 1 4 2 3 2, 1 (1) : 1 1 4 2 3 3, 2 (1) : 1 1 4 2 3 4, 3 (1) : 1 1 4 2 4 1, 1 (1) : 1 1 4 2 4 2, 2 (1) : 1 1 4 2 4 3, 3 (1) : 1 1 4 2 4 4, 3 (1) : 1 1 4 2 5 1, 1 (1) : 1 1 4 2 5 2, 2 (1) : 1 1 4 2 5 3, 3 (1) : 1 1 4 2 5 4, 3 (1) : 1 1 4 2 6 1, 1 (1) : 1 1 4 2 6 2, 1 (1) : 1 1 4 2 6 3, 2 (1) : 1 1 4 2 6 4, 3 (1) : 1 1 4 2 7 1, 1 (1) : 1 1 4 2 7 2, 2 (1) : 1 1 4 2 7 3, 2 (1) : 1 1 4 2 7 4, 3 (1) : 1 1 4 3 1 1, 2 (1) : 1 1 4 3 1 2, 2 (1) : 1 1 4 3 1 3, 3 (1) : 1 1 4 3 1 4, 3 (1) : 1 1 4 3 2 1, 2 (1) : 1 1 4 3 2 2, 2 (1) : 1 1 4 3 2 3, 3 (1) : 1 1 4 3 2 4, 3 (1) : 1 1 4 3 3 1, 2 (1) : 1

1 4 3 3 2, 2 (1) : 1 1 4 3 3 3, 3 (1) : 1 1 4 3 3 4, 3 (1) : 1 1 4 3 4 1, 2 (1) : 1 1 4 3 4 2, 3 (1) : 1 1 4 3 4 3, 3 (1) : 1 1 4 3 4 4, 3 (1) : 1 1 4 3 5 1, 2 (1) : 1 1 4 3 5 2, 3 (1) : 1 1 4 3 5 3, 3 (1) : 1 1 4 3 5 4, 3 (1) : 1 1 4 3 6 1, 1 (1) : 1 1 4 3 6 2, 2 (1) : 1 1 4 3 6 3, 2 (1) : 1 1 4 3 6 4, 3 (1) : 1 1 4 3 7 1, 1 (1) : 1 1 4 3 7 2, 2 (1) : 1 1 4 3 7 3, 2 (1) : 1 1 4 3 7 4, 3 (1) : 1 1 4 4 1 1, 1 (1) : 1 1 4 4 1 2, 2 (1) : 1 1 4 4 1 3, 3 (1) : 1 1 4 4 1 4, 3 (1) : 1 1 4 4 2 1, 1 (1) : 1 1 4 4 2 2, 2 (1) : 1 1 4 4 2 3, 3 (1) : 1 1 4 4 2 4, 3 (1) : 1 1 4 4 3 1, 1 (1) : 1 1 4 4 3 2, 1 (1) : 1 1 4 4 3 3, 2 (1) : 1 1 4 4 3 4, 3 (1) : 1 1 4 4 4 1, 2 (1) : 1 1 4 4 4 2, 3 (1) : 1 1 4 4 4 3, 3 (1) : 1 1 4 4 4 4, 3 (1) : 1 1 4 4 5 1, 2 (1) : 1 1 4 4 5 2, 3 (1) : 1 1 4 4 5 3, 3 (1) : 1 1 4 4 5 4, 3 (1) : 1 1 4 4 6 1, 1 (1) : 1 1 4 4 6 2, 2 (1) : 1 1 4 4 6 3, 2 (1) : 1 1 4 4 6 4, 3 (1) : 1 1 4 4 7 1, 1 (1) : 1 1 4 4 7 2, 2 (1) : 1 1 4 4 7 3, 2 (1) : 1 1 4 4 7 4, 3 (1) : 1 1 5 1 1 1, 1 (1) : 1 1 5 1 1 2, 2 (1) : 1 1 5 1 1 3, 2 (1) : 1 1 5 1 1 4, 3 (1) : 1 1 5 1 2 1, 1 (1) : 1 1 5 1 2 2, 2 (1) : 1 1 5 1 2 3, 2 (1) : 1 1 5 1 2 4, 3 (1) : 1 1 5 1 3 1, 1 (1) : 1 1 5 1 3 2, 1 (1) : 1 1 5 1 3 3, 2 (1) : 1 1 5 1 3 4, 3 (1) : 1 1 5 1 4 1, 2 (1) : 1

1 5 1 4 2, 3 (1) : 1 1 5 1 4 3, 3 (1) : 1 1 5 1 4 4, 3 (1) : 1 1 5 1 5 1, 2 (1) : 1 1 5 1 5 2, 3 (1) : 1 1 5 1 5 3, 3 (1) : 1 1 5 1 5 4, 3 (1) : 1 1 5 1 6 1, 1 (1) : 1 1 5 1 6 2, 2 (1) : 1 1 5 1 6 3, 3 (1) : 1 1 5 1 6 4, 3 (1) : 1 1 5 1 7 1, 1 (1) : 1 1 5 1 7 2, 2 (1) : 1 1 5 1 7 3, 3 (1) : 1 1 5 1 7 4, 3 (1) : 1 1 5 2 1 1, 1 (1) : 1 1 5 2 1 2, 2 (1) : 1 1 5 2 1 3, 3 (1) : 1 1 5 2 1 4, 3 (1) : 1 1 5 2 2 1, 1 (1) : 1 1 5 2 2 2, 2 (1) : 1 1 5 2 2 3, 3 (1) : 1 1 5 2 2 4, 3 (1) : 1 1 5 2 3 1, 1 (1) : 1 1 5 2 3 2, 1 (1) : 1 1 5 2 3 3, 2 (1) : 1 1 5 2 3 4, 3 (1) : 1 1 5 2 4 1, 2 (1) : 1 1 5 2 4 2, 3 (1) : 1 1 5 2 4 3, 3 (1) : 1 1 5 2 4 4, 3 (1) : 1 1 5 2 5 1, 2 (1) : 1 1 5 2 5 2, 3 (1) : 1 1 5 2 5 3, 3 (1) : 1 1 5 2 5 4, 3 (1) : 1 1 5 2 6 1, 1 (1) : 1 1 5 2 6 2, 1 (1) : 1 1 5 2 6 3, 2 (1) : 1 1 5 2 6 4, 3 (1) : 1 1 5 2 7 1, 1 (1) : 1 1 5 2 7 2, 1 (1) : 1 1 5 2 7 3, 2 (1) : 1 1 5 2 7 4, 3 (1) : 1 1 5 3 1 1, 1 (1) : 1 1 5 3 1 2, 2 (1) : 1 1 5 3 1 3, 3 (1) : 1 1 5 3 1 4, 3 (1) : 1 1 5 3 2 1, 1 (1) : 1 1 5 3 2 2, 2 (1) : 1 1 5 3 2 3, 3 (1) : 1 1 5 3 2 4, 3 (1) : 1 1 5 3 3 1, 1 (1) : 1 1 5 3 3 2, 1 (1) : 1 1 5 3 3 3, 2 (1) : 1 1 5 3 3 4, 3 (1) : 1 1 5 3 4 1, 1 (1) : 1 1 5 3 4 2, 2 (1) : 1 1 5 3 4 3, 3 (1) : 1 1 5 3 4 4, 3 (1) : 1 1 5 3 5 1, 1 (1) : 1

1 5 3 5 2, 2 (1) : 1 1 5 3 5 3, 3 (1) : 1 1 5 3 5 4, 3 (1) : 1 1 5 3 6 1, 1 (1) : 1 1 5 3 6 2, 1 (1) : 1 1 5 3 6 3, 2 (1) : 1 1 5 3 6 4, 3 (1) : 1 1 5 3 7 1, 1 (1) : 1 1 5 3 7 2, 1 (1) : 1 1 5 3 7 3, 2 (1) : 1 1 5 3 7 4, 3 (1) : 1 1 5 4 1 1, 1 (1) : 1 1 5 4 1 2, 2 (1) : 1 1 5 4 1 3, 3 (1) : 1 1 5 4 1 4, 3 (1) : 1 1 5 4 2 1, 1 (1) : 1 1 5 4 2 2, 2 (1) : 1 1 5 4 2 3, 2 (1) : 1 1 5 4 2 4, 3 (1) : 1 1 5 4 3 1, 1 (1) : 1 1 5 4 3 2, 1 (1) : 1 1 5 4 3 3, 2 (1) : 1 1 5 4 3 4, 3 (1) : 1 1 5 4 4 1, 1 (1) : 1 1 5 4 4 2, 2 (1) : 1 1 5 4 4 3, 3 (1) : 1 1 5 4 4 4, 3 (1) : 1 1 5 4 5 1, 1 (1) : 1 1 5 4 5 2, 2 (1) : 1 1 5 4 5 3, 3 (1) : 1 1 5 4 5 4, 3 (1) : 1 1 5 4 6 1, 1 (1) : 1 1 5 4 6 2, 1 (1) : 1 1 5 4 6 3, 2 (1) : 1 1 5 4 6 4, 3 (1) : 1 1 5 4 7 1, 1 (1) : 1 1 5 4 7 2, 2 (1) : 1 1 5 4 7 3, 3 (1) : 1 1 5 4 7 4, 3 (1) : 1 2 1 1 1 1, 1 (1) : 1 2 1 1 1 2, 2 (1) : 1 2 1 1 1 3, 2 (1) : 1 2 1 1 1 4, 3 (1) : 1 2 1 1 2 1, 1 (1) : 1 2 1 1 2 2, 1 (1) : 1 2 1 1 2 3, 2 (1) : 1 2 1 1 2 4, 2 (1) : 1 2 1 1 3 1, 1 (1) : 1 2 1 1 3 2, 1 (1) : 1 2 1 1 3 3, 2 (1) : 1 2 1 1 3 4, 2 (1) : 1 2 1 1 4 1, 1 (1) : 1 2 1 1 4 2, 2 (1) : 1 2 1 1 4 3, 3 (1) : 1 2 1 1 4 4, 3 (1) : 1 2 1 1 5 1, 1 (1) : 1 2 1 1 5 2, 2 (1) : 1 2 1 1 5 3, 3 (1) : 1 2 1 1 5 4, 3 (1) : 1 2 1 1 6 1, 1 (1) : 1

2 1 1 6 2, 1 (1) : 1 2 1 1 6 3, 2 (1) : 1 2 1 1 6 4, 2 (1) : 1 2 1 1 7 1, 1 (1) : 1 2 1 1 7 2, 1 (1) : 1 2 1 1 7 3, 2 (1) : 1 2 1 1 7 4, 2 (1) : 1 2 1 2 1 1, 1 (1) : 1 2 1 2 1 2, 1 (1) : 1 2 1 2 1 3, 2 (1) : 1 2 1 2 1 4, 3 (1) : 1 2 1 2 2 1, 1 (1) : 1 2 1 2 2 2, 1 (1) : 1 2 1 2 2 3, 2 (1) : 1 2 1 2 2 4, 2 (1) : 1 2 1 2 3 1, 1 (1) : 1 2 1 2 3 2, 1 (1) : 1 2 1 2 3 3, 2 (1) : 1 2 1 2 3 4, 2 (1) : 1 2 1 2 4 1, 1 (1) : 1 2 1 2 4 2, 2 (1) : 1 2 1 2 4 3, 3 (1) : 1 2 1 2 4 4, 3 (1) : 1 2 1 2 5 1, 1 (1) : 1 2 1 2 5 2, 2 (1) : 1 2 1 2 5 3, 3 (1) : 1 2 1 2 5 4, 3 (1) : 1 2 1 2 6 1, 1 (1) : 1 2 1 2 6 2, 1 (1) : 1 2 1 2 6 3, 2 (1) : 1 2 1 2 6 4, 2 (1) : 1 2 1 2 7 1, 1 (1) : 1 2 1 2 7 2, 1 (1) : 1 2 1 2 7 3, 2 (1) : 1 2 1 2 7 4, 2 (1) : 1 2 1 3 1 1, 1 (1) : 1 2 1 3 1 2, 2 (1) : 1 2 1 3 1 3, 3 (1) : 1 2 1 3 1 4, 3 (1) : 1 2 1 3 2 1, 1 (1) : 1 2 1 3 2 2, 2 (1) : 1 2 1 3 2 3, 3 (1) : 1 2 1 3 2 4, 3 (1) : 1 2 1 3 3 1, 1 (1) : 1 2 1 3 3 2, 1 (1) : 1 2 1 3 3 3, 2 (1) : 1 2 1 3 3 4, 3 (1) : 1 2 1 3 4 1, 2 (1) : 1 2 1 3 4 2, 2 (1) : 1 2 1 3 4 3, 3 (1) : 1 2 1 3 4 4, 3 (1) : 1 2 1 3 5 1, 2 (1) : 1 2 1 3 5 2, 2 (1) : 1 2 1 3 5 3, 3 (1) : 1 2 1 3 5 4, 3 (1) : 1 2 1 3 6 1, 1 (1) : 1 2 1 3 6 2, 1 (1) : 1 2 1 3 6 3, 2 (1) : 1 2 1 3 6 4, 2 (1) : 1 2 1 3 7 1, 1 (1) : 1

2 1 3 7 2, 1 (1) : 1 2 1 3 7 3, 2 (1) : 1 2 1 3 7 4, 2 (1) : 1 2 1 4 1 1, 1 (1) : 1 2 1 4 1 2, 1 (1) : 1 2 1 4 1 3, 2 (1) : 1 2 1 4 1 4, 3 (1) : 1 2 1 4 2 1, 1 (1) : 1 2 1 4 2 2, 1 (1) : 1 2 1 4 2 3, 2 (1) : 1 2 1 4 2 4, 3 (1) : 1 2 1 4 3 1, 1 (1) : 1 2 1 4 3 2, 1 (1) : 1 2 1 4 3 3, 2 (1) : 1 2 1 4 3 4, 3 (1) : 1 2 1 4 4 1, 2 (1) : 1 2 1 4 4 2, 2 (1) : 1 2 1 4 4 3, 3 (1) : 1 2 1 4 4 4, 3 (1) : 1 2 1 4 5 1, 2 (1) : 1 2 1 4 5 2, 2 (1) : 1 2 1 4 5 3, 3 (1) : 1 2 1 4 5 4, 3 (1) : 1 2 1 4 6 1, 1 (1) : 1 2 1 4 6 2, 1 (1) : 1 2 1 4 6 3, 2 (1) : 1 2 1 4 6 4, 3 (1) : 1 2 1 4 7 1, 1 (1) : 1 2 1 4 7 2, 1 (1) : 1 2 1 4 7 3, 2 (1) : 1 2 1 4 7 4, 3 (1) : 1 2 2 1 1 1, 1 (1) : 1 2 2 1 1 2, 2 (1) : 1 2 2 1 1 3, 3 (1) : 1 2 2 1 1 4, 3 (1) : 1 2 2 1 2 1, 1 (1) : 1 2 2 1 2 2, 2 (1) : 1 2 2 1 2 3, 3 (1) : 1 2 2 1 2 4, 3 (1) : 1 2 2 1 3 1, 1 (1) : 1 2 2 1 3 2, 1 (1) : 1 2 2 1 3 3, 2 (1) : 1 2 2 1 3 4, 3 (1) : 1 2 2 1 4 1, 1 (1) : 1 2 2 1 4 2, 2 (1) : 1 2 2 1 4 3, 3 (1) : 1 2 2 1 4 4, 3 (1) : 1 2 2 1 5 1, 1 (1) : 1 2 2 1 5 2, 2 (1) : 1 2 2 1 5 3, 3 (1) : 1 2 2 1 5 4, 3 (1) : 1 2 2 1 6 1, 1 (1) : 1 2 2 1 6 2, 1 (1) : 1 2 2 1 6 3, 2 (1) : 1 2 2 1 6 4, 2 (1) : 1 2 2 1 7 1, 1 (1) : 1 2 2 1 7 2, 2 (1) : 1 2 2 1 7 3, 3 (1) : 1 2 2 1 7 4, 3 (1) : 1 2 2 2 1 1, 1 (1) : 1

2 2 2 1 2, 2 (1) : 1 2 2 2 1 3, 3 (1) : 1 2 2 2 1 4, 3 (1) : 1 2 2 2 2 1, 1 (1) : 1 2 2 2 2 2, 2 (1) : 1 2 2 2 2 3, 3 (1) : 1 2 2 2 2 4, 3 (1) : 1 2 2 2 3 1, 1 (1) : 1 2 2 2 3 2, 1 (1) : 1 2 2 2 3 3, 2 (1) : 1 2 2 2 3 4, 3 (1) : 1 2 2 2 4 1, 2 (1) : 1 2 2 2 4 2, 2 (1) : 1 2 2 2 4 3, 3 (1) : 1 2 2 2 4 4, 3 (1) : 1 2 2 2 5 1, 1 (1) : 1 2 2 2 5 2, 2 (1) : 1 2 2 2 5 3, 3 (1) : 1 2 2 2 5 4, 3 (1) : 1 2 2 2 6 1, 1 (1) : 1 2 2 2 6 2, 2 (1) : 1 2 2 2 6 3, 3 (1) : 1 2 2 2 6 4, 3 (1) : 1 2 2 2 7 1, 1 (1) : 1 2 2 2 7 2, 2 (1) : 1 2 2 2 7 3, 3 (1) : 1 2 2 2 7 4, 3 (1) : 1 2 2 3 1 1, 2 (1) : 1 2 2 3 1 2, 2 (1) : 1 2 2 3 1 3, 3 (1) : 1 2 2 3 1 4, 3 (1) : 1 2 2 3 2 1, 1 (1) : 1 2 2 3 2 2, 2 (1) : 1 2 2 3 2 3, 3 (1) : 1 2 2 3 2 4, 3 (1) : 1 2 2 3 3 1, 1 (1) : 1 2 2 3 3 2, 2 (1) : 1 2 2 3 3 3, 3 (1) : 1 2 2 3 3 4, 3 (1) : 1 2 2 3 4 1, 2 (1) : 1 2 2 3 4 2, 3 (1) : 1 2 2 3 4 3, 3 (1) : 1 2 2 3 4 4, 3 (1) : 1 2 2 3 5 1, 2 (1) : 1 2 2 3 5 2, 3 (1) : 1 2 2 3 5 3, 3 (1) : 1 2 2 3 5 4, 3 (1) : 1 2 2 3 6 1, 1 (1) : 1 2 2 3 6 2, 1 (1) : 1 2 2 3 6 3, 2 (1) : 1 2 2 3 6 4, 2 (1) : 1 2 2 3 7 1, 1 (1) : 1 2 2 3 7 2, 2 (1) : 1 2 2 3 7 3, 2 (1) : 1 2 2 3 7 4, 3 (1) : 1 2 2 4 1 1, 1 (1) : 1 2 2 4 1 2, 1 (1) : 1 2 2 4 1 3, 2 (1) : 1 2 2 4 1 4, 2 (1) : 1 2 2 4 2 1, 1 (1) : 1

2 2 4 2 2, 1 (1) : 1 2 2 4 2 3, 2 (1) : 1 2 2 4 2 4, 2 (1) : 1 2 2 4 3 1, 1 (1) : 1 2 2 4 3 2, 1 (1) : 1 2 2 4 3 3, 2 (1) : 1 2 2 4 3 4, 2 (1) : 1 2 2 4 4 1, 1 (1) : 1 2 2 4 4 2, 2 (1) : 1 2 2 4 4 3, 3 (1) : 1 2 2 4 4 4, 3 (1) : 1 2 2 4 5 1, 1 (1) : 1 2 2 4 5 2, 1 (1) : 1 2 2 4 5 3, 2 (1) : 1 2 2 4 5 4, 2 (1) : 1 2 2 4 6 1, 1 (1) : 1 2 2 4 6 2, 1 (1) : 1 2 2 4 6 3, 2 (1) : 1 2 2 4 6 4, 2 (1) : 1 2 2 4 7 1, 1 (1) : 1 2 2 4 7 2, 1 (1) : 1 2 2 4 7 3, 2 (1) : 1 2 2 4 7 4, 2 (1) : 1 2 3 1 1 1, 1 (1) : 1 2 3 1 1 2, 2 (1) : 1 2 3 1 1 3, 3 (1) : 1 2 3 1 1 4, 3 (1) : 1 2 3 1 2 1, 1 (1) : 1 2 3 1 2 2, 2 (1) : 1 2 3 1 2 3, 3 (1) : 1 2 3 1 2 4, 3 (1) : 1 2 3 1 3 1, 1 (1) : 1 2 3 1 3 2, 1 (1) : 1 2 3 1 3 3, 2 (1) : 1 2 3 1 3 4, 3 (1) : 1 2 3 1 4 1, 2 (1) : 1 2 3 1 4 2, 2 (1) : 1 2 3 1 4 3, 3 (1) : 1 2 3 1 4 4, 3 (1) : 1 2 3 1 5 1, 2 (1) : 1 2 3 1 5 2, 2 (1) : 1 2 3 1 5 3, 3 (1) : 1 2 3 1 5 4, 3 (1) : 1 2 3 1 6 1, 1 (1) : 1 2 3 1 6 2, 1 (1) : 1 2 3 1 6 3, 2 (1) : 1 2 3 1 6 4, 3 (1) : 1 2 3 1 7 1, 1 (1) : 1 2 3 1 7 2, 1 (1) : 1 2 3 1 7 3, 2 (1) : 1 2 3 1 7 4, 3 (1) : 1 2 3 2 1 1, 1 (1) : 1 2 3 2 1 2, 2 (1) : 1 2 3 2 1 3, 3 (1) : 1 2 3 2 1 4, 3 (1) : 1 2 3 2 2 1, 1 (1) : 1 2 3 2 2 2, 2 (1) : 1 2 3 2 2 3, 3 (1) : 1 2 3 2 2 4, 3 (1) : 1 2 3 2 3 1, 1 (1) : 1

2 3 2 3 2, 2 (1) : 1 2 3 2 3 3, 3 (1) : 1 2 3 2 3 4, 3 (1) : 1 2 3 2 4 1, 2 (1) : 1 2 3 2 4 2, 3 (1) : 1 2 3 2 4 3, 3 (1) : 1 2 3 2 4 4, 3 (1) : 1 2 3 2 5 1, 2 (1) : 1 2 3 2 5 2, 3 (1) : 1 2 3 2 5 3, 3 (1) : 1 2 3 2 5 4, 3 (1) : 1 2 3 2 6 1, 1 (1) : 1 2 3 2 6 2, 1 (1) : 1 2 3 2 6 3, 2 (1) : 1 2 3 2 6 4, 3 (1) : 1 2 3 2 7 1, 1 (1) : 1 2 3 2 7 2, 2 (1) : 1 2 3 2 7 3, 3 (1) : 1 2 3 2 7 4, 3 (1) : 1 2 3 3 1 1, 1 (1) : 1 2 3 3 1 2, 2 (1) : 1 2 3 3 1 3, 3 (1) : 1 2 3 3 1 4, 3 (1) : 1 2 3 3 2 1, 1 (1) : 1 2 3 3 2 2, 2 (1) : 1 2 3 3 2 3, 3 (1) : 1 2 3 3 2 4, 3 (1) : 1 2 3 3 3 1, 1 (1) : 1 2 3 3 3 2, 2 (1) : 1 2 3 3 3 3, 3 (1) : 1 2 3 3 3 4, 3 (1) : 1 2 3 3 4 1, 2 (1) : 1 2 3 3 4 2, 3 (1) : 1 2 3 3 4 3, 3 (1) : 1 2 3 3 4 4, 3 (1) : 1 2 3 3 5 1, 2 (1) : 1 2 3 3 5 2, 3 (1) : 1 2 3 3 5 3, 3 (1) : 1 2 3 3 5 4, 3 (1) : 1 2 3 3 6 1, 2 (1) : 1 2 3 3 6 2, 2 (1) : 1 2 3 3 6 3, 3 (1) : 1 2 3 3 6 4, 3 (1) : 1 2 3 3 7 1, 2 (1) : 1 2 3 3 7 2, 2 (1) : 1 2 3 3 7 3, 3 (1) : 1 2 3 3 7 4, 3 (1) : 1 2 3 4 1 1, 1 (1) : 1 2 3 4 1 2, 2 (1) : 1 2 3 4 1 3, 3 (1) : 1 2 3 4 1 4, 3 (1) : 1 2 3 4 2 1, 1 (1) : 1 2 3 4 2 2, 2 (1) : 1 2 3 4 2 3, 3 (1) : 1 2 3 4 2 4, 3 (1) : 1 2 3 4 3 1, 1 (1) : 1 2 3 4 3 2, 2 (1) : 1 2 3 4 3 3, 2 (1) : 1 2 3 4 3 4, 3 (1) : 1 2 3 4 4 1, 2 (1) : 1

2 3 4 4 2, 2 (1) : 1 2 3 4 4 3, 3 (1) : 1 2 3 4 4 4, 3 (1) : 1 2 3 4 5 1, 1 (1) : 1 2 3 4 5 2, 2 (1) : 1 2 3 4 5 3, 3 (1) : 1 2 3 4 5 4, 3 (1) : 1 2 3 4 6 1, 1 (1) : 1 2 3 4 6 2, 2 (1) : 1 2 3 4 6 3, 2 (1) : 1 2 3 4 6 4, 3 (1) : 1 2 3 4 7 1, 1 (1) : 1 2 3 4 7 2, 2 (1) : 1 2 3 4 7 3, 2 (1) : 1 2 3 4 7 4, 3 (1) : 1 2 4 1 1 1, 2 (1) : 1 2 4 1 1 2, 2 (1) : 1 2 4 1 1 3, 3 (1) : 1 2 4 1 1 4, 3 (1) : 1 2 4 1 2 1, 2 (1) : 1 2 4 1 2 2, 2 (1) : 1 2 4 1 2 3, 3 (1) : 1 2 4 1 2 4, 3 (1) : 1 2 4 1 3 1, 1 (1) : 1 2 4 1 3 2, 2 (1) : 1 2 4 1 3 3, 2 (1) : 1 2 4 1 3 4, 3 (1) : 1 2 4 1 4 1, 2 (1) : 1 2 4 1 4 2, 2 (1) : 1 2 4 1 4 3, 3 (1) : 1 2 4 1 4 4, 3 (1) : 1 2 4 1 5 1, 2 (1) : 1 2 4 1 5 2, 2 (1) : 1 2 4 1 5 3, 3 (1) : 1 2 4 1 5 4, 3 (1) : 1 2 4 1 6 1, 1 (1) : 1 2 4 1 6 2, 2 (1) : 1 2 4 1 6 3, 3 (1) : 1 2 4 1 6 4, 3 (1) : 1 2 4 1 7 1, 2 (1) : 1 2 4 1 7 2, 2 (1) : 1 2 4 1 7 3, 3 (1) : 1 2 4 1 7 4, 3 (1) : 1 2 4 2 1 1, 2 (1) : 1 2 4 2 1 2, 2 (1) : 1 2 4 2 1 3, 3 (1) : 1 2 4 2 1 4, 3 (1) : 1 2 4 2 2 1, 2 (1) : 1 2 4 2 2 2, 2 (1) : 1 2 4 2 2 3, 3 (1) : 1 2 4 2 2 4, 3 (1) : 1 2 4 2 3 1, 1 (1) : 1 2 4 2 3 2, 2 (1) : 1 2 4 2 3 3, 3 (1) : 1 2 4 2 3 4, 3 (1) : 1 2 4 2 4 1, 2 (1) : 1 2 4 2 4 2, 3 (1) : 1 2 4 2 4 3, 3 (1) : 1 2 4 2 4 4, 3 (1) : 1 2 4 2 5 1, 2 (1) : 1

2 4 2 5 2, 3 (1) : 1 2 4 2 5 3, 3 (1) : 1 2 4 2 5 4, 3 (1) : 1 2 4 2 6 1, 1 (1) : 1 2 4 2 6 2, 1 (1) : 1 2 4 2 6 3, 2 (1) : 1 2 4 2 6 4, 3 (1) : 1 2 4 2 7 1, 2 (1) : 1 2 4 2 7 2, 2 (1) : 1 2 4 2 7 3, 3 (1) : 1 2 4 2 7 4, 3 (1) : 1 2 4 3 1 1, 2 (1) : 1 2 4 3 1 2, 3 (1) : 1 2 4 3 1 3, 3 (1) : 1 2 4 3 1 4, 3 (1) : 1 2 4 3 2 1, 2 (1) : 1 2 4 3 2 2, 2 (1) : 1 2 4 3 2 3, 3 (1) : 1 2 4 3 2 4, 3 (1) : 1 2 4 3 3 1, 1 (1) : 1 2 4 3 3 2, 2 (1) : 1 2 4 3 3 3, 2 (1) : 1 2 4 3 3 4, 3 (1) : 1 2 4 3 4 1, 2 (1) : 1 2 4 3 4 2, 3 (1) : 1 2 4 3 4 3, 3 (1) : 1 2 4 3 4 4, 3 (1) : 1 2 4 3 5 1, 2 (1) : 1 2 4 3 5 2, 3 (1) : 1 2 4 3 5 3, 3 (1) : 1 2 4 3 5 4, 3 (1) : 1 2 4 3 6 1, 1 (1) : 1 2 4 3 6 2, 2 (1) : 1 2 4 3 6 3, 2 (1) : 1 2 4 3 6 4, 3 (1) : 1 2 4 3 7 1, 1 (1) : 1 2 4 3 7 2, 2 (1) : 1 2 4 3 7 3, 3 (1) : 1 2 4 3 7 4, 3 (1) : 1 2 4 4 1 1, 2 (1) : 1 2 4 4 1 2, 2 (1) : 1 2 4 4 1 3, 3 (1) : 1 2 4 4 1 4, 3 (1) : 1 2 4 4 2 1, 2 (1) : 1 2 4 4 2 2, 2 (1) : 1 2 4 4 2 3, 3 (1) : 1 2 4 4 2 4, 3 (1) : 1 2 4 4 3 1, 1 (1) : 1 2 4 4 3 2, 2 (1) : 1 2 4 4 3 3, 2 (1) : 1 2 4 4 3 4, 3 (1) : 1 2 4 4 4 1, 2 (1) : 1 2 4 4 4 2, 3 (1) : 1 2 4 4 4 3, 3 (1) : 1 2 4 4 4 4, 3 (1) : 1 2 4 4 5 1, 2 (1) : 1 2 4 4 5 2, 3 (1) : 1 2 4 4 5 3, 3 (1) : 1 2 4 4 5 4, 3 (1) : 1 2 4 4 6 1, 1 (1) : 1

2 4 4 6 2, 1 (1) : 1 2 4 4 6 3, 2 (1) : 1 2 4 4 6 4, 3 (1) : 1 2 4 4 7 1, 1 (1) : 1 2 4 4 7 2, 2 (1) : 1 2 4 4 7 3, 2 (1) : 1 2 4 4 7 4, 3 (1) : 1 2 5 1 1 1, 1 (1) : 1 2 5 1 1 2, 2 (1) : 1 2 5 1 1 3, 3 (1) : 1 2 5 1 1 4, 3 (1) : 1 2 5 1 2 1, 1 (1) : 1 2 5 1 2 2, 2 (1) : 1 2 5 1 2 3, 3 (1) : 1 2 5 1 2 4, 3 (1) : 1 2 5 1 3 1, 1 (1) : 1 2 5 1 3 2, 1 (1) : 1 2 5 1 3 3, 2 (1) : 1 2 5 1 3 4, 3 (1) : 1 2 5 1 4 1, 2 (1) : 1 2 5 1 4 2, 3 (1) : 1 2 5 1 4 3, 3 (1) : 1 2 5 1 4 4, 3 (1) : 1 2 5 1 5 1, 2 (1) : 1 2 5 1 5 2, 3 (1) : 1 2 5 1 5 3, 3 (1) : 1 2 5 1 5 4, 3 (1) : 1 2 5 1 6 1, 1 (1) : 1 2 5 1 6 2, 2 (1) : 1 2 5 1 6 3, 2 (1) : 1 2 5 1 6 4, 3 (1) : 1 2 5 1 7 1, 1 (1) : 1 2 5 1 7 2, 2 (1) : 1 2 5 1 7 3, 3 (1) : 1 2 5 1 7 4, 3 (1) : 1 2 5 2 1 1, 1 (1) : 1 2 5 2 1 2, 2 (1) : 1 2 5 2 1 3, 3 (1) : 1 2 5 2 1 4, 3 (1) : 1 2 5 2 2 1, 1 (1) : 1 2 5 2 2 2, 2 (1) : 1 2 5 2 2 3, 3 (1) : 1 2 5 2 2 4, 3 (1) : 1 2 5 2 3 1, 1 (1) : 1 2 5 2 3 2, 1 (1) : 1 2 5 2 3 3, 2 (1) : 1 2 5 2 3 4, 3 (1) : 1 2 5 2 4 1, 2 (1) : 1 2 5 2 4 2, 3 (1) : 1 2 5 2 4 3, 3 (1) : 1 2 5 2 4 4, 3 (1) : 1 2 5 2 5 1, 2 (1) : 1 2 5 2 5 2, 3 (1) : 1 2 5 2 5 3, 3 (1) : 1 2 5 2 5 4, 3 (1) : 1 2 5 2 6 1, 1 (1) : 1 2 5 2 6 2, 1 (1) : 1 2 5 2 6 3, 2 (1) : 1 2 5 2 6 4, 3 (1) : 1 2 5 2 7 1, 1 (1) : 1

2 5 2 7 2, 2 (1) : 1 2 5 2 7 3, 3 (1) : 1 2 5 2 7 4, 3 (1) : 1 2 5 3 1 1, 2 (1) : 1 2 5 3 1 2, 2 (1) : 1 2 5 3 1 3, 3 (1) : 1 2 5 3 1 4, 3 (1) : 1 2 5 3 2 1, 2 (1) : 1 2 5 3 2 2, 2 (1) : 1 2 5 3 2 3, 3 (1) : 1 2 5 3 2 4, 3 (1) : 1 2 5 3 3 1, 1 (1) : 1 2 5 3 3 2, 2 (1) : 1 2 5 3 3 3, 2 (1) : 1 2 5 3 3 4, 3 (1) : 1 2 5 3 4 1, 2 (1) : 1 2 5 3 4 2, 3 (1) : 1 2 5 3 4 3, 3 (1) : 1 2 5 3 4 4, 3 (1) : 1 2 5 3 5 1, 2 (1) : 1 2 5 3 5 2, 2 (1) : 1 2 5 3 5 3, 3 (1) : 1 2 5 3 5 4, 3 (1) : 1 2 5 3 6 1, 1 (1) : 1 2 5 3 6 2, 1 (1) : 1 2 5 3 6 3, 2 (1) : 1 2 5 3 6 4, 3 (1) : 1 2 5 3 7 1, 1 (1) : 1 2 5 3 7 2, 1 (1) : 1 2 5 3 7 3, 2 (1) : 1 2 5 3 7 4, 3 (1) : 1 2 5 4 1 1, 2 (1) : 1 2 5 4 1 2, 2 (1) : 1 2 5 4 1 3, 3 (1) : 1 2 5 4 1 4, 3 (1) : 1 2 5 4 2 1, 1 (1) : 1 2 5 4 2 2, 2 (1) : 1 2 5 4 2 3, 3 (1) : 1 2 5 4 2 4, 3 (1) : 1 2 5 4 3 1, 1 (1) : 1 2 5 4 3 2, 2 (1) : 1 2 5 4 3 3, 2 (1) : 1 2 5 4 3 4, 3 (1) : 1 2 5 4 4 1, 2 (1) : 1 2 5 4 4 2, 2 (1) : 1 2 5 4 4 3, 3 (1) : 1 2 5 4 4 4, 3 (1) : 1 2 5 4 5 1, 2 (1) : 1 2 5 4 5 2, 2 (1) : 1 2 5 4 5 3, 3 (1) : 1 2 5 4 5 4, 3 (1) : 1 2 5 4 6 1, 1 (1) : 1 2 5 4 6 2, 2 (1) : 1 2 5 4 6 3, 2 (1) : 1 2 5 4 6 4, 3 (1) : 1 2 5 4 7 1, 1 (1) : 1 2 5 4 7 2, 2 (1) : 1 2 5 4 7 3, 3 (1) : 1 2 5 4 7 4, 3 (1) : 1 2 1 5 1 1, 1 (1) : 1

2 1 5 1 2, 1 (1) : 1 2 1 5 1 3, 2 (1) : 1 2 1 5 1 4, 2 (1) : 1 2 1 5 2 1, 1 (1) : 1 2 1 5 2 2, 1 (1) : 1 2 1 5 2 3, 1 (1) : 1 2 1 5 2 4, 2 (1) : 1 2 1 5 3 1, 1 (1) : 1 2 1 5 3 2, 1 (1) : 1 2 1 5 3 3, 1 (1) : 1 2 1 5 3 4, 2 (1) : 1 2 1 5 4 1, 1 (1) : 1 2 1 5 4 2, 1 (1) : 1 2 1 5 4 3, 2 (1) : 1 2 1 5 4 4, 2 (1) : 1 2 1 5 5 1, 1 (1) : 1 2 1 5 5 2, 1 (1) : 1 2 1 5 5 3, 2 (1) : 1 2 1 5 5 4, 2 (1) : 1 2 1 5 6 1, 1 (1) : 1 2 1 5 6 2, 1 (1) : 1 2 1 5 6 3, 1 (1) : 1 2 1 5 6 4, 2 (1) : 1 2 1 5 7 1, 1 (1) : 1 2 1 5 7 2, 1 (1) : 1 2 1 5 7 3, 1 (1) : 1 2 1 5 7 4, 2 (1) : 1 2 2 5 1 1, 1 (1) : 1 2 2 5 1 2, 1 (1) : 1 2 2 5 1 3, 2 (1) : 1 2 2 5 1 4, 2 (1) : 1 2 2 5 2 1, 1 (1) : 1 2 2 5 2 2, 1 (1) : 1 2 2 5 2 3, 1 (1) : 1 2 2 5 2 4, 2 (1) : 1 2 2 5 3 1, 1 (1) : 1 2 2 5 3 2, 1 (1) : 1 2 2 5 3 3, 1 (1) : 1 2 2 5 3 4, 2 (1) : 1 2 2 5 4 1, 1 (1) : 1 2 2 5 4 2, 1 (1) : 1 2 2 5 4 3, 2 (1) : 1 2 2 5 4 4, 2 (1) : 1 2 2 5 5 1, 1 (1) : 1 2 2 5 5 2, 1 (1) : 1 2 2 5 5 3, 2 (1) : 1 2 2 5 5 4, 2 (1) : 1 2 2 5 6 1, 1 (1) : 1 2 2 5 6 2, 1 (1) : 1 2 2 5 6 3, 1 (1) : 1 2 2 5 6 4, 2 (1) : 1 2 2 5 7 1, 1 (1) : 1 2 2 5 7 2, 1 (1) : 1 2 2 5 7 3, 1 (1) : 1 2 2 5 7 4, 2 (1) : 1 2 3 5 1 1, 1 (1) : 1 2 3 5 1 2, 1 (1) : 1 2 3 5 1 3, 2 (1) : 1 2 3 5 1 4, 3 (1) : 1 2 3 5 2 1, 1 (1) : 1

2 3 5 2 2, 1 (1) : 1 2 3 5 2 3, 2 (1) : 1 2 3 5 2 4, 2 (1) : 1 2 3 5 3 1, 1 (1) : 1 2 3 5 3 2, 1 (1) : 1 2 3 5 3 3, 2 (1) : 1 2 3 5 3 4, 2 (1) : 1 2 3 5 4 1, 1 (1) : 1 2 3 5 4 2, 1 (1) : 1 2 3 5 4 3, 2 (1) : 1 2 3 5 4 4, 3 (1) : 1 2 3 5 5 1, 1 (1) : 1 2 3 5 5 2, 1 (1) : 1 2 3 5 5 3, 2 (1) : 1 2 3 5 5 4, 3 (1) : 1 2 3 5 6 1, 1 (1) : 1 2 3 5 6 2, 1 (1) : 1 2 3 5 6 3, 2 (1) : 1 2 3 5 6 4, 3 (1) : 1 2 3 5 7 1, 1 (1) : 1 2 3 5 7 2, 1 (1) : 1 2 3 5 7 3, 2 (1) : 1 2 3 5 7 4, 2 (1) : 1 2 4 5 1 1, 1 (1) : 1 2 4 5 1 2, 1 (1) : 1 2 4 5 1 3, 2 (1) : 1 2 4 5 1 4, 3 (1) : 1 2 4 5 2 1, 1 (1) : 1 2 4 5 2 2, 1 (1) : 1 2 4 5 2 3, 2 (1) : 1 2 4 5 2 4, 2 (1) : 1 2 4 5 3 1, 1 (1) : 1 2 4 5 3 2, 1 (1) : 1 2 4 5 3 3, 2 (1) : 1 2 4 5 3 4, 2 (1) : 1 2 4 5 4 1, 1 (1) : 1 2 4 5 4 1, 1 (1) : 1 2 4 5 4 2, 1 (1) : 1 2 4 5 4 3, 2 (1) : 1 2 4 5 4 4, 3 (1) : 1 2 4 5 5 1, 1 (1) : 1 2 4 5 5 2, 1 (1) : 1 2 4 5 5 4, 3 (1) : 1 2 4 5 6 1, 1 (1) : 1 2 4 5 6 2, 1 (1) : 1 2 4 5 6 3, 2 (1) : 1 2 4 5 6 4, 2 (1) : 1 2 4 5 7 1, 1 (1) : 1 2 4 5 7 2, 1 (1) : 1 2 4 5 7 3, 2 (1) : 1 2 4 5 7 4, 2 (1) : 1 2 5 5 1 1, 1 (1) : 1 2 5 5 1 2, 1 (1) : 1 2 5 5 1 3, 2 (1) : 1 2 5 5 1 4, 2 (1) : 1 2 5 5 2 1, 1 (1) : 1 2 5 5 2 2, 1 (1) : 1 2 5 5 2 3, 2 (1) : 1 2 5 5 2 4, 2 (1) : 1 2 5 5 3 1, 1 (1) : 1

2 5 5 3 2, 1 (1) : 1 2 5 5 3 3, 2 (1) : 1 2 5 5 3 4, 2 (1) : 1 2 5 5 4 1, 1 (1) : 1 2 5 5 4 2, 1 (1) : 1 2 5 5 4 3, 2 (1) : 1 2 5 5 4 4, 3 (1) : 1 2 5 5 5 1, 1 (1) : 1 2 5 5 5 2, 1 (1) : 1 2 5 5 5 3, 2 (1) : 1 2 5 5 5 4, 3 (1) : 1 2 5 5 6 1, 1 (1) : 1 2 5 5 6 2, 1 (1) : 1 2 5 5 6 3, 1 (1) : 1 2 5 5 6 4, 2 (1) : 1 2 5 5 7 1, 1 (1) : 1 2 5 5 7 2, 1 (1) : 1 2 5 5 7 3, 2 (1) : 1 2 5 5 7 4, 2 (1) : 1 0 1 5 1 1, 1 (1) : 1 0 1 5 1 2, 1 (1) : 1 0 1 5 1 3, 2 (1) : 1 0 1 5 1 4, 3 (1) : 1 0 1 5 2 1, 1 (1) : 1 0 1 5 2 2, 1 (1) : 1 0 1 5 2 3, 2 (1) : 1 0 1 5 2 4, 2 (1) : 1 0 1 5 3 1, 1 (1) : 1 0 1 5 3 2, 1 (1) : 1 0 1 5 3 3, 2 (1) : 1 0 1 5 3 4, 2 (1) : 1 0 1 5 4 1, 1 (1) : 1 0 1 5 4 2, 1 (1) : 1 0 1 5 4 3, 2 (1) : 1 0 1 5 4 4, 3 (1) : 1 0 1 5 5 1, 1 (1) : 1 0 1 5 5 2, 1 (1) : 1 0 1 5 5 3, 2 (1) : 1 0 1 5 5 4, 3 (1) : 1 0 1 5 6 1, 1 (1) : 1 0 1 5 6 2, 1 (1) : 1 0 1 5 6 3, 2 (1) : 1 0 1 5 6 4, 2 (1) : 1 0 1 5 7 1, 1 (1) : 1 0 1 5 7 2, 1 (1) : 1 0 1 5 7 3, 2 (1) : 1 0 1 5 7 4, 2 (1) : 1 0 2 5 1 1, 1 (1) : 1 0 2 5 1 2, 1 (1) : 1 0 2 5 1 3, 2 (1) : 1 0 2 5 1 4, 3 (1) : 1 0 2 5 2 1, 1 (1) : 1 0 2 5 2 2, 1 (1) : 1 0 2 5 2 3, 2 (1) : 1 0 2 5 2 4, 2 (1) : 1 0 2 5 3 1, 1 (1) : 1 0 2 5 3 2, 1 (1) : 1 0 2 5 3 3, 2 (1) : 1 0 2 5 3 4, 2 (1) : 1 0 2 5 4 1, 1 (1) : 1

0 2 5 4 2, 1 (1) : 1 0 2 5 4 3, 2 (1) : 1 0 2 5 4 4, 3 (1) : 1 0 2 5 5 1, 1 (1) : 1 0 2 5 5 2, 1 (1) : 1 0 2 5 5 3, 2 (1) : 1 0 2 5 5 4, 3 (1) : 1 0 2 5 6 1, 1 (1) : 1 0 2 5 6 2, 1 (1) : 1 0 2 5 6 3, 2 (1) : 1 0 2 5 6 4, 2 (1) : 1 0 2 5 7 1, 1 (1) : 1 0 2 5 7 2, 1 (1) : 1 0 2 5 7 3, 2 (1) : 1 0 2 5 7 4, 2 (1) : 1 0 3 5 1 1, 1 (1) : 1 0 3 5 1 2, 1 (1) : 1 0 3 5 1 3, 2 (1) : 1 0 3 5 1 4, 3 (1) : 1 0 3 5 2 1, 1 (1) : 1 0 3 5 2 2, 1 (1) : 1 0 3 5 2 3, 2 (1) : 1 0 3 5 2 4, 3 (1) : 1 0 3 5 3 1, 1 (1) : 1 0 3 5 3 2, 1 (1) : 1 0 3 5 3 3, 2 (1) : 1 0 3 5 3 4, 3 (1) : 1 0 3 5 4 1, 1 (1) : 1 0 3 5 4 2, 1 (1) : 1 0 3 5 4 3, 3 (1) : 1 0 3 5 4 4, 3 (1) : 1 0 3 5 5 1, 1 (1) : 1 0 3 5 5 2, 1 (1) : 1 0 3 5 5 3, 3 (1) : 1 0 3 5 5 4, 3 (1) : 1 0 3 5 6 1, 1 (1) : 1 0 3 5 6 2, 1 (1) : 1 0 3 5 6 3, 2 (1) : 1 0 3 5 6 4, 3 (1) : 1 0 3 5 7 1, 1 (1) : 1 0 3 5 7 2, 1 (1) : 1 0 3 5 7 3, 2 (1) : 1 0 3 5 7 4, 3 (1) : 1 0 4 5 1 1, 1 (1) : 1 0 4 5 1 2, 1 (1) : 1 0 4 5 1 3, 2 (1) : 1 0 4 5 1 4, 3 (1) : 1 0 4 5 2 1, 1 (1) : 1 0 4 5 2 2, 1 (1) : 1 0 4 5 2 3, 2 (1) : 1 0 4 5 2 4, 3 (1) : 1 0 4 5 3 1, 1 (1) : 1 0 4 5 3 2, 1 (1) : 1 0 4 5 3 3, 2 (1) : 1 0 4 5 3 4, 3 (1) : 1 0 4 5 4 1, 1 (1) : 1 0 4 5 4 2, 1 (1) : 1 0 4 5 4 3, 3 (1) : 1 0 4 5 4 4, 3 (1) : 1 0 4 5 5 1, 1 (1) : 1

0 4 5 5 2, 1 (1) : 1 0 4 5 5 3, 3 (1) : 1 0 4 5 5 4, 3 (1) : 1 0 4 5 6 1, 1 (1) : 1 0 4 5 6 2, 1 (1) : 1 0 4 5 6 3, 2 (1) : 1 0 4 5 6 4, 3 (1) : 1 0 4 5 7 1, 1 (1) : 1 0 4 5 7 2, 1 (1) : 1 0 4 5 7 3, 2 (1) : 1 0 4 5 7 4, 3 (1) : 1 0 5 5 1 1, 1 (1) : 1 0 5 5 1 2, 1 (1) : 1 0 5 5 1 3, 2 (1) : 1 0 5 5 1 4, 3 (1) : 1 0 5 5 2 1, 1 (1) : 1 0 5 5 2 2, 1 (1) : 1 0 5 5 2 3, 2 (1) : 1 0 5 5 2 4, 3 (1) : 1 0 5 5 3 1, 1 (1) : 1 0 5 5 3 2, 1 (1) : 1 0 5 5 3 3, 2 (1) : 1 0 5 5 3 4, 3 (1) : 1 0 5 5 4 1, 1 (1) : 1 0 5 5 4 2, 1 (1) : 1 0 5 5 4 3, 3 (1) : 1 0 5 5 4 4, 3 (1) : 1 0 5 5 5 1, 1 (1) : 1 0 5 5 5 2, 1 (1) : 1 0 5 5 5 3, 3 (1) : 1 0 5 5 5 4, 3 (1) : 1 0 5 5 6 1, 1 (1) : 1 0 5 5 6 2, 1 (1) : 1 0 5 5 6 3, 2 (1) : 1 0 5 5 6 4, 3 (1) : 1 0 5 5 7 1, 1 (1) : 1 0 5 5 7 2, 1 (1) : 1 0 5 5 7 3, 2 (1) : 1 0 5 5 7 4, 3 (1) : 1 E.2 Biological Features Type II Diabetes Mellitus Tendency Fuzzy Inference System

[SYSTEM] NAME='BIOLOGICALFEATURESDIABETESTENDENCY' TYPE='MAMDANI' VERSION=2.0 NUMINPUTS=5 NUMOUTPUTS=1 NUMRULES=288 ANDMETHOD='MIN' ORMETHOD='MAX' IMPMETHOD='MIN' AGGMETHOD='MAX' DEFUZZMETHOD='CENTROID'

[INPUT1] NAME='SYSTOLICBLOODPRESSURE' RANGE=[-1 300] NUMMFS=4 MF1='LOW':'TRIMF',[-1 49.17 119.4] MF2='MEDIUM':'TRIMF',[118.4 124.3 129.4] MF3='HIGH':'TRIMF',[128.4 134.3 139.5] MF4='VERYHIGH':'TRIMF',[138.5 224.8 300] [INPUT2] NAME='DIASTOLICBLOODPRESSURE' RANGE=[-1 200] NUMMFS=4 MF1='LOW':'TRIMF',[-1 39.2 79.4] MF2='MEDIUM':'TRIMF',[78.4 84.93 89.45] MF3='HIGH':'TRIMF',[88.45 109.6 119.6] MF4='VERYHIGH':'TRIMF',[118.6 159.8 200] [INPUT3] NAME='CHOLESTEROLLEVEL' RANGE=[-1 500] NUMMFS=3 MF1='LOW':'TRIMF',[-1 99.2 199.4] MF2='MEDIUM':'TRIMF',[198.4 218.8 239.5] MF3='HIGH':'TRIMF',[238.5 359.7 500] [INPUT4] NAME='BLOODGLUCOSELEVEL' RANGE=[-1 800] NUMMFS=3 MF1='LOW':'TRIMF',[-1 49.06 98.12] MF2='MEDIUM':'TRIMF',[97.12 115 124.2] MF3='HIGH':'TRIMF',[123.2 449.6 800] [INPUT5] NAME='PREGNANCYSITUATION' RANGE=[0 100] NUMMFS=2 MF1='NEGATIVE':'TRIMF',[0 0 50] MF2='POSITIVE':'TRIMF',[49 100 100] [OUTPUT1] NAME='BIOLOGICALFEATURESDIABETESTENDENCY' RANGE=[0 100] NUMMFS=3 MF1='LOW':'TRIMF',[-40 0 40] MF2='MEDIUM':'TRIMF',[10 50 90] MF3='HIGH':'TRIMF',[60 100 140] [RULES] 1 1 1 1 2, 1 (1) : 1 1 1 1 1 1, 1 (1) : 1 1 1 1 2 2, 3 (1) : 1 1 1 1 2 1, 1 (1) : 1 1 1 1 3 2, 3 (1) : 1 1 1 1 3 1, 3 (1) : 1 1 1 2 1 2, 1 (1) : 1 1 1 2 1 1, 1 (1) : 1

1 1 2 2 2, 3 (1) : 1 1 1 2 2 1, 3 (1) : 1 1 1 2 3 2, 3 (1) : 1 1 1 2 3 1, 3 (1) : 1 1 1 3 1 2, 1 (1) : 1 1 1 3 1 1, 1 (1) : 1 1 1 3 2 2, 3 (1) : 1 1 1 3 2 1, 2 (1) : 1 1 1 3 3 2, 3 (1) : 1 1 1 3 3 1, 3 (1) : 1 1 2 1 1 2, 1 (1) : 1 1 2 1 1 1, 1 (1) : 1 1 2 1 2 2, 3 (1) : 1 1 2 1 2 1, 2 (1) : 1 1 2 1 3 2, 3 (1) : 1 1 2 1 3 1, 3 (1) : 1 1 2 2 1 2, 1 (1) : 1 1 2 2 1 1, 1 (1) : 1 1 2 2 2 2, 3 (1) : 1 1 2 2 2 1, 2 (1) : 1 1 2 2 3 2, 3 (1) : 1 1 2 2 3 1, 3 (1) : 1 1 2 3 1 2, 1 (1) : 1 1 2 3 1 1, 1 (1) : 1 1 2 3 2 2, 3 (1) : 1 1 2 3 2 1, 2 (1) : 1 1 2 3 3 2, 3 (1) : 1 1 2 3 3 1, 3 (1) : 1 1 3 1 1 2, 1 (1) : 1 1 3 1 1 1, 1 (1) : 1 1 3 1 2 2, 3 (1) : 1 1 3 1 2 1, 2 (1) : 1 1 3 1 3 2, 3 (1) : 1 1 3 1 3 1, 3 (1) : 1 1 3 2 1 2, 1 (1) : 1 1 3 2 1 1, 1 (1) : 1 1 3 2 2 2, 3 (1) : 1 1 3 2 2 1, 2 (1) : 1 1 3 2 3 2, 3 (1) : 1 1 3 2 3 1, 3 (1) : 1 1 3 3 1 2, 1 (1) : 1 1 3 3 1 1, 1 (1) : 1 1 3 3 2 2, 3 (1) : 1 1 3 3 2 1, 2 (1) : 1 1 3 3 3 2, 3 (1) : 1 1 3 3 3 1, 3 (1) : 1 1 4 1 1 2, 1 (1) : 1 1 4 1 1 1, 1 (1) : 1 1 4 1 2 2, 2 (1) : 1 1 4 1 2 1, 2 (1) : 1 1 4 1 3 2, 3 (1) : 1 1 4 1 3 1, 3 (1) : 1 1 4 2 1 2, 1 (1) : 1 1 4 2 1 1, 1 (1) : 1 1 4 2 2 2, 2 (1) : 1 1 4 2 2 1, 1 (1) : 1 1 4 2 3 2, 3 (1) : 1 1 4 2 3 1, 3 (1) : 1 1 4 3 1 2, 1 (1) : 1 1 4 3 1 1, 1 (1) : 1

1 4 3 2 2, 3 (1) : 1 1 4 3 2 1, 2 (1) : 1 1 4 3 3 2, 3 (1) : 1 1 4 3 3 1, 3 (1) : 1 2 1 1 1 2, 1 (1) : 1 2 1 1 1 1, 1 (1) : 1 2 1 1 2 2, 3 (1) : 1 2 1 1 2 1, 2 (1) : 1 2 1 1 3 2, 3 (1) : 1 2 1 1 3 1, 3 (1) : 1 2 1 2 1 2, 1 (1) : 1 2 1 2 1 1, 1 (1) : 1 2 1 2 2 2, 3 (1) : 1 2 1 2 2 1, 3 (1) : 1 2 1 2 3 2, 3 (1) : 1 2 1 2 3 1, 3 (1) : 1 2 1 3 1 2, 1 (1) : 1 2 1 3 1 1, 1 (1) : 1 2 1 3 2 2, 3 (1) : 1 2 1 3 2 1, 2 (1) : 1 2 1 3 3 2, 3 (1) : 1 2 1 3 3 1, 3 (1) : 1 2 2 1 1 2, 1 (1) : 1 2 2 1 1 1, 1 (1) : 1 2 2 1 2 2, 3 (1) : 1 2 2 1 2 1, 2 (1) : 1 2 2 1 3 2, 3 (1) : 1 2 2 1 3 1, 3 (1) : 1 2 2 2 1 2, 1 (1) : 1 2 2 2 1 1, 1 (1) : 1 2 2 2 2 2, 3 (1) : 1 2 2 2 2 1, 2 (1) : 1 2 2 2 3 2, 3 (1) : 1 2 2 2 3 1, 3 (1) : 1 2 2 3 1 2, 1 (1) : 1 2 2 3 1 1, 1 (1) : 1 2 2 3 2 2, 3 (1) : 1 2 2 3 2 1, 2 (1) : 1 2 2 3 3 2, 3 (1) : 1 2 2 3 3 1, 3 (1) : 1 2 3 1 1 2, 1 (1) : 1 2 3 1 1 1, 1 (1) : 1 2 3 1 2 2, 3 (1) : 1 2 3 1 2 1, 2 (1) : 1 2 3 1 3 2, 3 (1) : 1 2 3 1 3 1, 3 (1) : 1 2 3 2 1 2, 1 (1) : 1 2 3 2 1 1, 1 (1) : 1 2 3 2 2 2, 3 (1) : 1 2 3 2 2 1, 2 (1) : 1 2 3 2 3 2, 3 (1) : 1 2 3 2 3 1, 3 (1) : 1 2 3 3 1 2, 1 (1) : 1 2 3 3 1 1, 1 (1) : 1 2 3 3 2 2, 3 (1) : 1 2 3 3 2 1, 2 (1) : 1 2 3 3 3 2, 3 (1) : 1 2 3 3 3 1, 3 (1) : 1 2 4 1 1 2, 1 (1) : 1 2 4 1 1 1, 1 (1) : 1

2 4 1 2 2, 2 (1) : 1 2 4 1 2 1, 2 (1) : 1 2 4 1 3 2, 3 (1) : 1 2 4 1 3 1, 3 (1) : 1 2 4 2 1 2, 1 (1) : 1 2 4 2 1 1, 1 (1) : 1 2 4 2 2 2, 2 (1) : 1 2 4 2 2 1, 1 (1) : 1 2 4 2 3 2, 3 (1) : 1 2 4 2 3 1, 3 (1) : 1 2 4 3 1 2, 1 (1) : 1 2 4 3 1 1, 1 (1) : 1 2 4 3 2 2, 3 (1) : 1 2 4 3 2 1, 2 (1) : 1 2 4 3 3 2, 3 (1) : 1 2 4 3 3 1, 3 (1) : 1 3 1 1 1 2, 1 (1) : 1 3 1 1 1 1, 1 (1) : 1 3 1 1 2 2, 3 (1) : 1 3 1 1 2 1, 2 (1) : 1 3 1 1 3 2, 3 (1) : 1 3 1 1 3 1, 3 (1) : 1 3 1 2 1 2, 1 (1) : 1 3 1 2 1 1, 1 (1) : 1 3 1 2 2 2, 3 (1) : 1 3 1 2 2 1, 3 (1) : 1 3 1 2 3 2, 3 (1) : 1 3 1 2 3 1, 3 (1) : 1 3 1 3 1 2, 1 (1) : 1 3 1 3 1 1, 1 (1) : 1 3 1 3 2 2, 3 (1) : 1 3 1 3 2 1, 2 (1) : 1 3 1 3 3 2, 3 (1) : 1 3 1 3 3 1, 3 (1) : 1 3 2 1 1 2, 1 (1) : 1 3 2 1 1 1, 1 (1) : 1 3 2 1 2 2, 3 (1) : 1 3 2 1 2 1, 2 (1) : 1 3 2 1 3 2, 3 (1) : 1 3 2 1 3 1, 3 (1) : 1 3 2 2 1 2, 1 (1) : 1 3 2 2 1 1, 1 (1) : 1 3 2 2 2 2, 3 (1) : 1 3 2 2 2 1, 2 (1) : 1 3 2 2 3 2, 3 (1) : 1 3 2 2 3 1, 3 (1) : 1 3 2 3 1 2, 1 (1) : 1 3 2 3 1 1, 1 (1) : 1 3 2 3 2 2, 3 (1) : 1 3 2 3 2 1, 2 (1) : 1 3 2 3 3 2, 3 (1) : 1 3 2 3 3 1, 3 (1) : 1 3 3 1 1 2, 1 (1) : 1 3 3 1 1 1, 1 (1) : 1 3 3 1 2 2, 3 (1) : 1 3 3 1 2 1, 2 (1) : 1 3 3 1 3 2, 3 (1) : 1 3 3 1 3 1, 3 (1) : 1 3 3 2 1 2, 1 (1) : 1 3 3 2 1 1, 1 (1) : 1

3 3 2 2 2, 3 (1) : 1 3 3 2 2 1, 2 (1) : 1 3 3 2 3 2, 3 (1) : 1 3 3 2 3 1, 3 (1) : 1 3 3 3 1 2, 1 (1) : 1 3 3 3 1 1, 1 (1) : 1 3 3 3 2 2, 3 (1) : 1 3 3 3 2 1, 2 (1) : 1 3 3 3 3 2, 3 (1) : 1 3 3 3 3 1, 3 (1) : 1 3 4 1 1 2, 1 (1) : 1 3 4 1 1 1, 1 (1) : 1 3 4 1 2 2, 2 (1) : 1 3 4 1 2 1, 2 (1) : 1 3 4 1 3 2, 3 (1) : 1 3 4 1 3 1, 3 (1) : 1 3 4 2 1 2, 1 (1) : 1 3 4 2 1 1, 1 (1) : 1 3 4 2 2 2, 2 (1) : 1 3 4 2 2 1, 1 (1) : 1 3 4 2 3 2, 3 (1) : 1 3 4 2 3 1, 3 (1) : 1 3 4 3 1 2, 1 (1) : 1 3 4 3 1 1, 1 (1) : 1 3 4 3 2 2, 3 (1) : 1 3 4 3 2 1, 2 (1) : 1 3 4 3 3 2, 3 (1) : 1 3 4 3 3 1, 3 (1) : 1 4 1 1 1 2, 1 (1) : 1 4 1 1 1 1, 1 (1) : 1 4 1 1 2 2, 3 (1) : 1 4 1 1 2 1, 2 (1) : 1 4 1 1 3 2, 3 (1) : 1 4 1 1 3 1, 3 (1) : 1 4 1 2 1 2, 1 (1) : 1 4 1 2 1 1, 1 (1) : 1 4 1 2 2 2, 3 (1) : 1 4 1 2 2 1, 3 (1) : 1 4 1 2 3 2, 3 (1) : 1 4 1 2 3 1, 3 (1) : 1 4 1 3 1 2, 1 (1) : 1 4 1 3 1 1, 1 (1) : 1 4 1 3 2 2, 3 (1) : 1 4 1 3 2 1, 2 (1) : 1 4 1 3 3 2, 3 (1) : 1 4 1 3 3 1, 3 (1) : 1 4 2 1 1 2, 1 (1) : 1 4 2 1 1 1, 1 (1) : 1 4 2 1 2 2, 3 (1) : 1 4 2 1 2 1, 2 (1) : 1 4 2 1 3 2, 3 (1) : 1 4 2 1 3 1, 3 (1) : 1 4 2 2 1 2, 1 (1) : 1 4 2 2 1 1, 1 (1) : 1 4 2 2 2 2, 3 (1) : 1 4 2 2 2 1, 2 (1) : 1 4 2 2 3 2, 3 (1) : 1 4 2 2 3 1, 3 (1) : 1 4 2 3 1 2, 1 (1) : 1 4 2 3 1 1, 1 (1) : 1

4 2 3 2 2, 3 (1) : 1 4 2 3 2 1, 2 (1) : 1 4 2 3 3 2, 3 (1) : 1 4 2 3 3 1, 3 (1) : 1 4 3 1 1 2, 1 (1) : 1 4 3 1 1 1, 1 (1) : 1 4 3 1 2 2, 3 (1) : 1 4 3 1 2 1, 2 (1) : 1 4 3 1 3 2, 3 (1) : 1 4 3 1 3 1, 3 (1) : 1 4 3 2 1 2, 1 (1) : 1 4 3 2 1 1, 1 (1) : 1 4 3 2 2 2, 3 (1) : 1 4 3 2 2 1, 2 (1) : 1 4 3 2 3 2, 3 (1) : 1 4 3 2 3 1, 3 (1) : 1 4 3 3 1 2, 1 (1) : 1 4 3 3 1 1, 1 (1) : 1 4 3 3 2 2, 3 (1) : 1 4 3 3 2 1, 2 (1) : 1 4 3 3 3 2, 3 (1) : 1 4 3 3 3 1, 3 (1) : 1 4 4 1 1 2, 1 (1) : 1 4 4 1 1 1, 1 (1) : 1 4 4 1 2 2, 2 (1) : 1 4 4 1 2 1, 2 (1) : 1 4 4 1 3 2, 3 (1) : 1 4 4 1 3 1, 3 (1) : 1 4 4 2 1 2, 1 (1) : 1 4 4 2 1 1, 1 (1) : 1 4 4 2 2 2, 2 (1) : 1 4 4 2 2 1, 1 (1) : 1 4 4 2 3 2, 3 (1) : 1 4 4 2 3 1, 3 (1) : 1 4 4 3 1 2, 1 (1) : 1 4 4 3 1 1, 1 (1) : 1 4 4 3 2 2, 3 (1) : 1 4 4 3 2 1, 2 (1) : 1 4 4 3 3 2, 3 (1) : 1 4 4 3 3 1, 3 (1) : 1

E.3 Lifestyle Habits Type II Diabetes Mellitus Tendency Fuzzy Inference System

[SYSTEM] NAME='LIFESTYLEHABITSDIABETESTENDENCY' TYPE='MAMDANI' VERSION=2.0 NUMINPUTS=4 NUMOUTPUTS=1 NUMRULES=192 ANDMETHOD='MIN' ORMETHOD='MAX' IMPMETHOD='MIN' AGGMETHOD='MAX' DEFUZZMETHOD='CENTROID'

[INPUT1] NAME='EATINGHABIT' RANGE=[0 100] NUMMFS=3 MF1='UNHEALTHIER':'TRIMF',[-40 0 40] MF2='PARTIALHEALTHIER':'TRIMF',[10 50 90] MF3='HEALTHIER':'TRIMF',[59.91 99.91 139.9] [INPUT2] NAME='PHYSICALACTIVITYLEVEL' RANGE=[0 100] NUMMFS=4 MF1='SEDENTARY':'TRIMF',[-33.33 0 33.33] MF2='LOWACTIVITY':'TRIMF',[0 33.33 66.67] MF3='ACTIVE':'TRIMF',[33.33 66.67 100] MF4='VERYACTIVE':'TRIMF',[66.67 100 133.3] [INPUT3] NAME='SMOKINGHABIT' RANGE=[0 100] NUMMFS=4 MF1='NOUSAGE':'TRIMF',[-33.33 0 33.33] MF2='PASSIVEUSAGE':'TRIMF',[0 33.33 66.67] MF3='FORMERUSER':'TRIMF',[33.42 66.76 100.1] MF4='ACTIVEUSAGE':'TRIMF',[66.58 99.91 133.2] [INPUT4] NAME='ALCOHOLHABIT' RANGE=[0 100] NUMMFS=4 MF1='NOUSAGE':'TRIMF',[-33.33 0 33.33] MF2='LOWUSAGE':'TRIMF',[0 33.33 66.67] MF3='REGULARUSAGE':'TRIMF',[33.14 66.48 99.81] MF4='ADDICTED':'TRIMF',[66.58 99.91 133.2] [OUTPUT1] NAME='LIFESTYLEHABITSTENDENCY' RANGE=[0 100] NUMMFS=3 MF1='LOWRISK':'TRIMF',[-40 0 40] MF2='MEDIUMRISK':'TRIMF',[10 50 90] MF3='HIGHRISK':'TRIMF',[60 100 140] [RULES] 1 1 1 1, 1 (1) : 1 1 1 1 2, 2 (1) : 1 1 1 1 3, 3 (1) : 1 1 1 1 4, 3 (1) : 1 1 1 2 1, 2 (1) : 1 1 1 2 2, 2 (1) : 1 1 1 2 3, 3 (1) : 1 1 1 2 4, 3 (1) : 1 1 1 3 1, 2 (1) : 1 1 1 3 2, 2 (1) : 1 1 1 3 3, 3 (1) : 1 1 1 3 4, 3 (1) : 1 1 1 4 1, 2 (1) : 1 1 1 4 2, 2 (1) : 1 1 1 4 3, 3 (1) : 1

1 1 4 4, 3 (1) : 1 1 2 1 1, 1 (1) : 1 1 2 1 2, 1 (1) : 1 1 2 1 3, 2 (1) : 1 1 2 1 4, 2 (1) : 1 1 2 2 1, 1 (1) : 1 1 2 2 2, 2 (1) : 1 1 2 2 3, 2 (1) : 1 1 2 2 4, 2 (1) : 1 1 2 3 1, 1 (1) : 1 1 2 3 2, 2 (1) : 1 1 2 3 3, 2 (1) : 1 1 2 3 4, 2 (1) : 1 1 2 4 1, 1 (1) : 1 1 2 4 2, 2 (1) : 1 1 2 4 3, 2 (1) : 1 1 2 4 4, 3 (1) : 1 1 3 1 1, 1 (1) : 1 1 3 1 2, 1 (1) : 1 1 3 1 3, 1 (1) : 1 1 3 1 4, 2 (1) : 1 1 3 2 1, 1 (1) : 1 1 3 2 2, 2 (1) : 1 1 3 2 3, 3 (1) : 1 1 3 2 4, 3 (1) : 1 1 3 3 1, 1 (1) : 1 1 3 3 2, 2 (1) : 1 1 3 3 3, 3 (1) : 1 1 3 3 4, 3 (1) : 1 1 3 4 1, 2 (1) : 1 1 3 4 2, 2 (1) : 1 1 3 4 3, 3 (1) : 1 1 3 4 4, 3 (1) : 1 1 4 1 1, 1 (1) : 1 1 4 1 2, 1 (1) : 1 1 4 1 3, 1 (1) : 1 1 4 1 4, 2 (1) : 1 1 4 2 1, 1 (1) : 1 1 4 2 2, 1 (1) : 1 1 4 2 3, 2 (1) : 1 1 4 2 4, 2 (1) : 1 1 4 3 1, 1 (1) : 1 1 4 3 2, 1 (1) : 1 1 4 3 3, 2 (1) : 1 1 4 3 4, 2 (1) : 1 1 4 4 1, 1 (1) : 1 1 4 4 2, 2 (1) : 1 1 4 4 3, 2 (1) : 1 1 4 4 4, 2 (1) : 1 2 1 1 1, 1 (1) : 1 2 1 1 2, 2 (1) : 1 2 1 1 3, 3 (1) : 1 2 1 1 4, 3 (1) : 1 2 1 2 1, 1 (1) : 1 2 1 2 2, 2 (1) : 1 2 1 2 3, 2 (1) : 1 2 1 2 4, 3 (1) : 1 2 1 3 1, 1 (1) : 1 2 1 3 2, 2 (1) : 1 2 1 3 3, 2 (1) : 1

2 1 3 4, 3 (1) : 1 2 1 4 1, 1 (1) : 1 2 1 4 2, 2 (1) : 1 2 1 4 3, 3 (1) : 1 2 1 4 4, 3 (1) : 1 2 2 1 1, 1 (1) : 1 2 2 1 2, 1 (1) : 1 2 2 1 3, 2 (1) : 1 2 2 1 4, 2 (1) : 1 2 2 2 1, 1 (1) : 1 2 2 2 2, 2 (1) : 1 2 2 2 3, 2 (1) : 1 2 2 2 4, 3 (1) : 1 2 2 3 1, 1 (1) : 1 2 2 3 2, 2 (1) : 1 2 2 3 3, 2 (1) : 1 2 2 3 4, 3 (1) : 1 2 2 4 1, 2 (1) : 1 2 2 4 2, 2 (1) : 1 2 2 4 3, 3 (1) : 1 2 2 4 4, 3 (1) : 1 2 3 1 1, 1 (1) : 1 2 3 1 2, 1 (1) : 1 2 3 1 3, 1 (1) : 1 2 3 1 4, 2 (1) : 1 2 3 2 1, 1 (1) : 1 2 3 2 2, 1 (1) : 1 2 3 2 3, 1 (1) : 1 2 3 2 4, 2 (1) : 1 2 3 3 1, 1 (1) : 1 2 3 3 2, 1 (1) : 1 2 3 3 3, 1 (1) : 1 2 3 3 4, 2 (1) : 1 2 3 4 1, 1 (1) : 1 2 3 4 2, 1 (1) : 1 2 3 4 3, 2 (1) : 1 2 3 4 4, 2 (1) : 1 2 4 1 1, 1 (1) : 1 2 4 1 2, 1 (1) : 1 2 4 1 3, 1 (1) : 1 2 4 1 4, 1 (1) : 1 2 4 2 1, 1 (1) : 1 2 4 2 2, 1 (1) : 1 2 4 2 3, 2 (1) : 1 2 4 2 4, 2 (1) : 1 2 4 3 1, 1 (1) : 1 2 4 3 2, 1 (1) : 1 2 4 3 3, 2 (1) : 1 2 4 3 4, 2 (1) : 1 2 4 4 1, 1 (1) : 1 2 4 4 2, 1 (1) : 1 2 4 4 3, 2 (1) : 1 2 4 4 4, 2 (1) : 1 3 1 1 1, 1 (1) : 1 3 1 1 2, 1 (1) : 1 3 1 1 3, 1 (1) : 1 3 1 1 4, 1 (1) : 1 3 1 2 1, 1 (1) : 1 3 1 2 2, 1 (1) : 1 3 1 2 3, 1 (1) : 1

3 1 2 4, 2 (1) : 1 3 1 3 1, 1 (1) : 1 3 1 3 2, 1 (1) : 1 3 1 3 3, 1 (1) : 1 3 1 3 4, 2 (1) : 1 3 1 4 1, 1 (1) : 1 3 1 4 2, 1 (1) : 1 3 1 4 3, 2 (1) : 1 3 1 4 4, 3 (1) : 1 3 2 1 1, 1 (1) : 1 3 2 1 2, 1 (1) : 1 3 2 1 3, 1 (1) : 1 3 2 1 4, 2 (1) : 1 3 2 2 1, 1 (1) : 1 3 2 2 2, 1 (1) : 1 3 2 2 3, 2 (1) : 1 3 2 2 4, 2 (1) : 1 3 2 3 1, 1 (1) : 1 3 2 3 2, 1 (1) : 1 3 2 3 3, 2 (1) : 1 3 2 3 4, 2 (1) : 1 3 2 4 1, 1 (1) : 1 3 2 4 2, 2 (1) : 1 3 2 4 3, 2 (1) : 1 3 2 4 4, 2 (1) : 1 3 3 1 1, 1 (1) : 1 3 3 1 2, 1 (1) : 1 3 3 1 3, 1 (1) : 1 3 3 1 4, 2 (1) : 1 3 3 2 1, 1 (1) : 1 3 3 2 2, 1 (1) : 1 3 3 2 3, 2 (1) : 1 3 3 2 4, 2 (1) : 1 3 3 3 1, 1 (1) : 1 3 3 3 2, 1 (1) : 1 3 3 3 3, 2 (1) : 1 3 3 3 4, 2 (1) : 1 3 3 4 1, 1 (1) : 1 3 3 4 2, 2 (1) : 1 3 3 4 3, 3 (1) : 1 3 3 4 4, 3 (1) : 1 3 4 1 1, 1 (1) : 1 3 4 1 2, 1 (1) : 1 3 4 1 3, 1 (1) : 1 3 4 1 4, 1 (1) : 1 3 4 2 1, 1 (1) : 1 3 4 2 2, 1 (1) : 1 3 4 2 3, 1 (1) : 1 3 4 2 4, 2 (1) : 1 3 4 3 1, 1 (1) : 1 3 4 3 2, 1 (1) : 1 3 4 3 3, 1 (1) : 1 3 4 3 4, 2 (1) : 1 3 4 4 1, 1 (1) : 1 3 4 4 2, 1 (1) : 1 3 4 4 3, 2 (1) : 1 3 4 4 4, 2 (1) : 1

E.4 Primary Diagnosis Type II Diabetes Mellitus Tendency Fuzzy Inference System

[SYSTEM] NAME='THEMAINSYSTEMOFDIAGNOSISDIABETES' TYPE='MAMDANI' VERSION=2.0 NUMINPUTS=3 NUMOUTPUTS=1 NUMRULES=27 ANDMETHOD='MIN' ORMETHOD='MAX' IMPMETHOD='MIN' AGGMETHOD='MAX' DEFUZZMETHOD='CENTROID' [INPUT1] NAME='PERSONALFEATURES' RANGE=[0 100] NUMMFS=3 MF1='LOWRISK':'TRIMF',[-40 0 40] MF2='MEDIUMRISK':'TRIMF',[10 50 90] MF3='HIGHRISK':'TRIMF',[60 100 140] [INPUT2] NAME='BIOLOGICALFEATURES' RANGE=[0 100] NUMMFS=3 MF1='LOWRISK':'TRIMF',[-40 0 40] MF2='MEDIUMRISK':'TRIMF',[10 50 90] MF3='HIGHRISK':'TRIMF',[59.81 99.81 139.8] [INPUT3] NAME='LIFESTYLEHABITS' RANGE=[0 100] NUMMFS=3 MF1='LOWRISK':'TRIMF',[-40 0 40] MF2='MEDIUMRISK':'TRIMF',[9.907 49.91 89.91] MF3='HIGHRISK':'TRIMF',[60 100 140] [OUTPUT1] NAME='PATIENTSHEALTHSITUATION' RANGE=[0 100] NUMMFS=3 MF1='HEALTHIER':'TRIMF',[-40 0 40] MF2='PREDIABETES':'TRIMF',[10 50 90] MF3='TYPE2DIABETES':'TRIMF',[60 100 140] [RULES] 1 1 1, 1 (1) : 1 1 1 2, 1 (1) : 1 1 1 3, 1 (1) : 1 1 2 1, 1 (1) : 1 1 2 2, 2 (1) : 1 1 2 3, 2 (1) : 1 1 3 1, 3 (1) : 1 1 3 2, 3 (1) : 1 1 3 3, 3 (1) : 1 2 1 1, 1 (1) : 1

2 1 2, 1 (1) : 1 2 1 3, 1 (1) : 1 2 2 1, 2 (1) : 1 2 2 2, 2 (1) : 1 2 2 3, 3 (1) : 1 2 3 1, 3 (1) : 1 2 3 2, 3 (1) : 1 2 3 3, 3 (1) : 1 3 1 1, 1 (1) : 1 3 1 2, 1 (1) : 1 3 1 3, 2 (1) : 1 3 2 1, 2 (1) : 1 3 2 2, 3 (1) : 1 3 2 3, 3 (1) : 1 3 3 1, 3 (1) : 1 3 3 2, 3 (1) : 1 3 3 3, 3 (1) : 1

E.5 Secondary Diagnosis Type II Diabetes Mellitus Tendency Fuzzy Inference System

[SYSTEM] NAME='MAINSYSTEM2' TYPE='MAMDANI' VERSION=2.0 NUMINPUTS=2 NUMOUTPUTS=1 NUMRULES=9 ANDMETHOD='MIN' ORMETHOD='MAX' IMPMETHOD='MIN' AGGMETHOD='MAX' DEFUZZMETHOD='CENTROID' [INPUT1] NAME='PERSONAL' RANGE=[0 100] NUMMFS=3 MF1='LOW':'TRIMF',[-40 0 40] MF2='MEDIUM':'TRIMF',[10 50 90] MF3='HIGH':'TRIMF',[60 100 140] [INPUT2] NAME='BIOLOGICAL' RANGE=[0 100] NUMMFS=3 MF1='LOW':'TRIMF',[-40 0 40] MF2='MEDIUM':'TRIMF',[10 50 90] MF3='HIGH':'TRIMF',[60 100 140] [OUTPUT1] NAME='HEALTHSITUATION' RANGE=[0 100] NUMMFS=3 MF1='HEALTHY':'TRIMF',[-40 0 40] MF2='PREDIABETES':'TRIMF',[10 50 90] MF3='TYPE2':'TRIMF',[59.74 99.74 139.7]

[RULES] 1 1, 1 (1) : 1 1 2, 1 (1) : 1 1 3, 2 (1) : 1 2 1, 1 (1) : 1 2 2, 2 (1) : 1 2 3, 3 (1) : 1 3 1, 1 (1) : 1 3 2, 2 (1) : 1 3 3, 3 (1) : 1

*The format of the fuzzy conditional statements, which are mentioned in 4.2.6, has changed for

the index. The machine deals with the provided version. The numbers before the colon refer to

the index number of the membership functions.

APPENDIX F: THE INTERFACE SOFTWARE PROGRAM IN MATLAB

classdef LastUpdatedFebruaryType2DiabetesMellitusDiagnosis < matlab.apps.AppBase % Properties that correspond to app components properties (Access = public) UIFigure matlab.ui.Figure Type2DiagnoseTabGroup matlab.ui.container.TabGroup WelcomeTab matlab.ui.container.Tab StartButton matlab.ui.control.Button WelcomeSign matlab.ui.control.Label InformationTextArea matlab.ui.control.TextArea PersonalTab matlab.ui.container.Tab PersonalInformationSign matlab.ui.control.Label WARNINGBOXLabel matlab.ui.control.Label WarningBoxEditField matlab.ui.control.EditField PreviousButton matlab.ui.control.Button NextButton matlab.ui.control.Button ResetButton matlab.ui.control.Button GENDERListBoxLabel matlab.ui.control.Label GenderListBox matlab.ui.control.ListBox AGEEditFieldLabel matlab.ui.control.Label AgeEditField matlab.ui.control.NumericEditField FAMILYHISTORYLabel matlab.ui.control.Label FamilyHistoryListBox matlab.ui.control.ListBox NATIONALITYListBoxLabel matlab.ui.control.Label NationalityListBox matlab.ui.control.ListBox BODYMASSINDEXLabel matlab.ui.control.Label BodyMassIndexEditField matlab.ui.control.NumericEditField BiologicalTab matlab.ui.container.Tab BiologicalInformationSign matlab.ui.control.Label BLOODPRESSURELabel matlab.ui.control.Label CHOLESTEROLLEVELLabel matlab.ui.control.Label BLOODGLUCOSELEVELLabel matlab.ui.control.Label WARNINGBOXEditFieldLabel matlab.ui.control.Label WarningBoxEditField2 matlab.ui.control.EditField PreviousButton_2 matlab.ui.control.Button NextButton_2 matlab.ui.control.Button ResetButton_2 matlab.ui.control.Button SYSTOLICBLOODPRESSUREEditFieldLabel matlab.ui.control.Label SystolicBloodPressureEditField matlab.ui.control.NumericEditField DIASTOLICBLOODPRESSUREEditFieldLabel matlab.ui.control.Label DiastolicBloodPressureEditField matlab.ui.control.NumericEditField CHOLESTEROLLEVELLabel_2 matlab.ui.control.Label CholesterolEditField matlab.ui.control.NumericEditField BLOODGLUCOSELEVELEditFieldLabel matlab.ui.control.Label BloodGlucoseLevelEditField matlab.ui.control.NumericEditField PREGNANCYSITUATIONListBoxLabel matlab.ui.control.Label PregnancySituationListBox matlab.ui.control.ListBox QuestionTab matlab.ui.container.Tab

INFORMATIONCHOOSINGLabel matlab.ui.control.Label TextArea matlab.ui.control.TextArea YesButton matlab.ui.control.Button NoButton matlab.ui.control.Button PreviousButton_3 matlab.ui.control.Button NextButton_3 matlab.ui.control.Button LifestyleTab matlab.ui.container.Tab LIFESTYLEINFORMATIONLabel matlab.ui.control.Label PreviousButton_4 matlab.ui.control.Button NextButton_4 matlab.ui.control.Button ResetButton_3 matlab.ui.control.Button SMOKINGHABITListBoxLabel matlab.ui.control.Label SmokingHabitListBox matlab.ui.control.ListBox ALCOHOLCONSUMPTIONListBoxLabel matlab.ui.control.Label AlcoholConsumptionListBox matlab.ui.control.ListBox EatingHabitListBox matlab.ui.control.ListBox EATINGHABITORDIETARYRESTRICTIONLabel matlab.ui.control.Label PhysicalActivityLevelListBox matlab.ui.control.ListBox PHYSICALACTIVITYLEVELLabel matlab.ui.control.Label DiagnoseTab matlab.ui.container.Tab RESULTSLabel matlab.ui.control.Label DiagnoseButton matlab.ui.control.Button CloseButton matlab.ui.control.Button NewPatientButton matlab.ui.control.Button PERSONALFEATURESLabel matlab.ui.control.Label PersonalEditField matlab.ui.control.EditField BIOLOGICALFEATURESLabel matlab.ui.control.Label BiologicalEditField matlab.ui.control.EditField LIFESTYLEFEATURESLabel matlab.ui.control.Label LifeStyleEditField matlab.ui.control.EditField RESULTSLabel_2 matlab.ui.control.Label ResultsEditField matlab.ui.control.EditField end properties (Access = private) personal pfis biological bfis lifestyle lfis mainone mofis maintwo mtfis Gender Age FamilyHistory Nationality BodyMassIndex Systolic Diastolic BloodGlucoseLevel

CholesterolLevel PregnancySituation PhysicalActivityLevel Dietary SmokingHabit AlcoholConsumption % Description end methods (Access = private) % Button pushed function: StartButton function StartButtonPushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.PersonalTab; end % Value changed function: GenderListBox function GenderListBoxValueChanged(app, event) app.Gender.Value = str2double(app.GenderListBox.Value); end % Value changed function: AgeEditField function AgeEditFieldValueChanged(app, event) app.Age.Value = app.AgeEditField.Value; if app.Age.Value <20 app.WarningBoxEditField.Value='Patients should be older than 20 years old!'; elseif app.Age.Value>100 app.WarningBoxEditField.Value='Patients should be younger than 100 years old!'; end end % Value changed function: FamilyHistoryListBox function FamilyHistoryListBoxValueChanged(app, event) app.FamilyHistory.Value = str2double(app.FamilyHistoryListBox.Value); end % Value changed function: NationalityListBox function NationalityListBoxValueChanged(app, event) app.Nationality.Value = str2double(app.NationalityListBox.Value); end % Value changed function: BodyMassIndexEditField function BodyMassIndexEditFieldValueChanged(app, event) app.BodyMassIndex.Value = app.BodyMassIndexEditField.Value; if app.BodyMassIndex.Value<0 app.WarningBoxEditField.Value='Body Mass Index should be higher than 0!'; elseif app.BodyMassIndex.Value>50 app.WarningBoxEditField.Value='Body Mass Index should be lower than 50!'; end end % Button pushed function: PreviousButton function PreviousButtonPushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.WelcomeTab;

end % Button pushed function: ResetButton function ResetButtonPushed(app, event) app.Gender.Value=[]; set(handle(app.GenderListBox), 'Value', []); app.AgeEditField.Value=0; app.Age.Value=0; app.FamilyHistory.Value=0; set(handle(app.FamilyHistoryListBox),'Value',[]); app.Nationality.Value=0; set(handle(app.NationalityListBox),'Value',[]); app.BodyMassIndexEditField.Value=0; app.BodyMassIndex.Value=0; app.WarningBoxEditField.Value=" "; app.PersonalEditField.Value=""; end % Button pushed function: NextButton function NextButtonPushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.BiologicalTab; app.pfis=readfis('PersonalFeaturesDiabetesTendency'); app.personal= evalfis([app.Gender.Value app.Age.Value app.FamilyHistory.Value app.Nationality.Value app.BodyMassIndex.Value], app.pfis); if app.personal <34 app.PersonalEditField.Value='Low Personal Risk'; elseif app.personal >=34 && app.personal<67 app.PersonalEditField.Value='Medium Personal Risk'; elseif app.personal >=67 app.PersonalEditField.Value='High Personal Risk'; end end % Value changed function: SystolicBloodPressureEditField function SystolicBloodPressureEditFieldValueChanged(app, event) app.Systolic.Value = app.SystolicBloodPressureEditField.Value; if app.Systolic.Value<0 app.WarningBoxEditField2.Value='Systolic Blood Pressure should be higher than 0!'; elseif app.Systolic.Value>300 app.WarningBoxEditField2.Value='Systolic Blood Pressure should be lower than 300!'; end end % Value changed function: DiastolicBloodPressureEditField function DiastolicBloodPressureEditFieldValueChanged(app, event) app.Diastolic.Value = app.DiastolicBloodPressureEditField.Value; if app.Diastolic.Value <0 app.WarningBoxEditField2.Value='Diastolic Blood Pressure should be higher than 0!'; elseif app.Diastolic.Value>200 app.WarningBoxEditField2.Value='Diastolic Blood Pressure should be lower than 200!'; end

end % Value changed function: CholesterolEditField function CholesterolEditFieldValueChanged(app, event) app.CholesterolLevel.Value = app.CholesterolEditField.Value; if app.CholesterolLevel.Value <0 app.WarningBoxEditField2.Value='Cholesterol Level should be higher than 0!'; elseif app.CholesterolLevel.Value>500 app.WarningBoxEditField2.Value='Cholesterol Level should be lower than 500!'; end end % Value changed function: BloodGlucoseLevelEditField function BloodGlucoseLevelEditFieldValueChanged(app, event) app.BloodGlucoseLevel.Value = app.BloodGlucoseLevelEditField.Value; if app.BloodGlucoseLevel.Value <0 app.WarningBoxEditField2.Value='Blood Glucose Level should be higher than 0!'; elseif app.BloodGlucoseLevel.Value>800 app.WarningBoxEditField2.Value='Blood Glucose Level should be lower than 800!'; end end % Value changed function: PregnancySituationListBox function PregnancySituationListBoxValueChanged(app, event) app.PregnancySituation.Value = str2double(app.PregnancySituationListBox.Value); end % Button pushed function: PreviousButton_2 function PreviousButton_2Pushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.PersonalTab; end % Button pushed function: ResetButton_2 function ResetButton_2Pushed(app, event) app.Systolic.Value=0; app.SystolicBloodPressureEditField.Value=0; app.Diastolic.Value=0; app.DiastolicBloodPressureEditField.Value=0; app.CholesterolEditField.Value=0; app.CholesterolLevel.Value=0; app.BloodGlucoseLevelEditField.Value=0; app.BloodGlucoseLevel.Value=0; app.PregnancySituation.Value=0; set(handle(app.PregnancySituationListBox),'Value',[]); app.WarningBoxEditField2.Value=""; app.BiologicalEditField.Value=""; end % Button pushed function: NextButton_2 function NextButton_2Pushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.QuestionTab; app.bfis=readfis('BiologicalFeaturesDiabetesTendency');

app.biological=evalfis([app.Systolic.Value app.Diastolic.Value app.CholesterolLevel.Value app.BloodGlucoseLevel.Value app.PregnancySituation.Value], app.bfis); if app.biological <34 app.BiologicalEditField.Value='Low Biological Risk'; elseif app.biological >=34 && app.biological<67 app.BiologicalEditField.Value='Medium Biological Risk'; elseif app.biological >=67 app.BiologicalEditField.Value='High Biological Risk'; end end % Button pushed function: YesButton function YesButtonPushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.LifestyleTab; end % Button pushed function: NoButton function NoButtonPushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.DiagnoseTab; app.lifestyle=0; app.LifeStyleEditField.Value="Patient's lifestyle habit is unknown"; end % Button pushed function: PreviousButton_3 function PreviousButton_3Pushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.BiologicalTab; end % Button pushed function: NextButton_3 function NextButton_3Pushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.LifestyleTab; end % Value changed function: PhysicalActivityLevelListBox function PhysicalActivityLevelListBoxValueChanged(app, event) app.PhysicalActivityLevel.Value = str2double(app.PhysicalActivityLevelListBox.Value); end % Value changed function: EatingHabitListBox function EatingHabitListBoxValueChanged(app, event) app.Dietary.Value = str2double(app.EatingHabitListBox.Value); end % Value changed function: SmokingHabitListBox function SmokingHabitListBoxValueChanged(app, event) app.SmokingHabit.Value = str2double(app.SmokingHabitListBox.Value); end % Value changed function: AlcoholConsumptionListBox function AlcoholConsumptionListBoxValueChanged(app, event) app.AlcoholConsumption.Value = str2double(app.AlcoholConsumptionListBox.Value); end % Button pushed function: PreviousButton_4 function PreviousButton_4Pushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.QuestionTab;

end % Button pushed function: ResetButton_3 function ResetButton_3Pushed(app, event) app.PhysicalActivityLevel.Value=0; set(handle(app.PhysicalActivityLevelListBox),'Value',[]); app.Dietary.Value=0; set(handle(app.EatingHabitListBox),'Value',[]); app.SmokingHabit.Value=0; set(handle(app.SmokingHabitListBox),'Value',[]); app.AlcoholConsumption.Value=0; set(handle(app.AlcoholConsumptionListBox),'Value',[]); app.LifeStyleEditField.Value=""; end % Button pushed function: NextButton_4 function NextButton_4Pushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.DiagnoseTab; app.lfis=readfis('LifestyleHabitsDiabetesTendency'); app.lifestyle=evalfis([app.Dietary.Value app.PhysicalActivityLevel.Value app.SmokingHabit.Value app.AlcoholConsumption.Value], app.lfis); if app.lifestyle <34 app.LifeStyleEditField.Value='Low Lifestyle Factors Risk'; elseif app.lifestyle >=34 && app.lifestyle<67 app.LifeStyleEditField.Value='Medium Lifestyle Factors Risk'; elseif app.lifestyle >=67 app.LifeStyleEditField.Value='High Lifestyle Factors Risk'; end end % Button pushed function: DiagnoseButton function DiagnoseButtonPushed(app, event) if app.lifestyle==0 app.mtfis=readfis('MainSystem2'); app.maintwo=evalfis([app.personal app.biological], app.mtfis); if app.maintwo<40 set(app.ResultsEditField,'FontColor',[0 1 0]); app.ResultsEditField.Value='The Patient is healthier!! Stay Healthy!'; elseif app.maintwo >=40 && app.maintwo<60 set(app.ResultsEditField,'FontColor',[1 0 0]); app.ResultsEditField.Value='Possible Pre-Diabetes Patient! Contact with your doctor!'; elseif app.maintwo >=60 set(app.ResultsEditField,'FontColor',[1 0 0]); app.ResultsEditField.Value='Possible Type II Diabetes Mellitus Patient! Contact with your doctor!'; end elseif app.lifestyle>0 app.mofis=readfis('TheMainSystemofDiagnosisDiabetes'); app.mainone=evalfis([app.personal app.biological app.lifestyle], app.mofis); if app.mainone<40 set(app.ResultsEditField,'FontColor',[0 1 0]);

app.ResultsEditField.Value='The Patient is healthier!! Stay Healthy!'; elseif app.mainone >=40 && app.mainone <60 set(app.ResultsEditField,'FontColor',[1 0 0]); app.ResultsEditField.Value='Possible Pre-Diabetes Patient! Contact with your doctor!'; elseif app.mainone >=60 set(app.ResultsEditField,'FontColor',[1 0 0]); app.ResultsEditField.Value='Possible Type II Diabetes Mellitus Patient! Contact with your doctor!'; end end end % Button pushed function: CloseButton function CloseButtonPushed(app, event) answer = questdlg('Would you like to close application?','Yes','No'); % Handle response switch answer case 'Yes' app.delete; case 'No' app.Type2DiagnoseTabGroup.SelectedTab=app.DiagnoseTab; case 'Cancel' app.Type2DiagnoseTabGroup.SelectedTab=app.DiagnoseTab; end end % Button pushed function: NewPatientButton function NewPatientButtonPushed(app, event) app.Type2DiagnoseTabGroup.SelectedTab=app.WelcomeTab; app.Gender.Value=[]; set(handle(app.GenderListBox), 'Value', []) app.AgeEditField.Value=0; app.Age.Value=0; app.FamilyHistory.Value=0; set(handle(app.FamilyHistoryListBox),'Value',[]); app.Nationality.Value=0; set(handle(app.NationalityListBox),'Value',[]); app.BodyMassIndexEditField.Value=0; app.BodyMassIndex.Value=0; app.WarningBoxEditField.Value=" "; app.Systolic.Value=0; app.SystolicBloodPressureEditField.Value=0; app.Diastolic.Value=0; app.DiastolicBloodPressureEditField.Value=0; app.CholesterolEditField.Value=0; app.CholesterolLevel.Value=0; app.BloodGlucoseLevelEditField.Value=0; app.BloodGlucoseLevel.Value=0; app.PregnancySituation.Value=0; set(handle(app.PregnancySituationListBox),'Value',[]); app.WarningBoxEditField2.Value="";

app.PhysicalActivityLevel.Value=0; set(handle(app.PhysicalActivityLevelListBox),'Value',[]); app.Dietary.Value=0; set(handle(app.EatingHabitListBox),'Value',[]); app.SmokingHabit.Value=0; set(handle(app.SmokingHabitListBox),'Value',[]); app.AlcoholConsumption.Value=0; set(handle(app.AlcoholConsumptionListBox),'Value',[]); app.PersonalEditField.Value=""; app.BiologicalEditField.Value=""; app.LifeStyleEditField.Value=""; app.ResultsEditField.Value=""; end end % App initialization and construction methods (Access = private) % Create UIFigure and components function createComponents(app) % Create UIFigure app.UIFigure = uifigure; app.UIFigure.Position = [100 100 805 765]; app.UIFigure.Name = 'UI Figure'; % Create Type2DiagnoseTabGroup app.Type2DiagnoseTabGroup = uitabgroup(app.UIFigure); app.Type2DiagnoseTabGroup.TabLocation = 'left'; app.Type2DiagnoseTabGroup.Position = [1 -2 805 768]; % Create WelcomeTab app.WelcomeTab = uitab(app.Type2DiagnoseTabGroup); app.WelcomeTab.Title = 'WELCOME'; % Create StartButton app.StartButton = uibutton(app.WelcomeTab, 'push'); app.StartButton.ButtonPushedFcn = createCallbackFcn(app, @StartButtonPushed, true); app.StartButton.FontSize = 20; app.StartButton.FontWeight = 'bold'; app.StartButton.Position = [561 16 129 41]; app.StartButton.Text = 'START'; % Create WelcomeSign app.WelcomeSign = uilabel(app.WelcomeTab); app.WelcomeSign.HorizontalAlignment = 'center'; app.WelcomeSign.FontSize = 26; app.WelcomeSign.FontWeight = 'bold'; app.WelcomeSign.Position = [1 664 701 105]; app.WelcomeSign.Text = {'WELCOME TO'; 'THE TYPE II DIABETES MELLITUS'; 'DIAGNOSIS SYSTEM'}; % Create InformationTextArea app.InformationTextArea = uitextarea(app.WelcomeTab); app.InformationTextArea.Editable = 'off'; app.InformationTextArea.FontSize = 20; app.InformationTextArea.BackgroundColor = [0.9412 0.9412 0.9412]; app.InformationTextArea.Position = [0 78 702 572];

app.InformationTextArea.Value = {''; '1- The following application will help you to the diagnostic of type II diabetes mellitus. However, please be aware of that the system uses intelligent system techniques. If the result shows that the patient has possible type II diabetes mellitus or pre-diabetes, please be contact with your physicians urgently.'; ''; '2- The system comprises of the four steps. The first step covers the patients'' personal features. The patients'' biological features is in the second step of the system. Third step is the last step of diagnosis, and it covers the patients'' life style habits. After these three steps, the system will evaluate the patients'' information and show in the evaluation system page. Also, if you do not know patient''s lifestyle habit information, please choose "No" button in the question screen.'; ''; '3-The evaluation system screen shows the risk levels for every features and explain the possible result of the patient. If you want to start re-evaluation, please use the "New Patient" button.'; ''; '4- If the patient suffered from gestational diabetes, pregnancy situation should be "Positive".'; ''; '5- If you understand the system, please click Start button for evaluation.'; ''; 'STAY HEALTHY!!'}; % Create PersonalTab app.PersonalTab = uitab(app.Type2DiagnoseTabGroup); app.PersonalTab.Title = 'PERSONAL'; % Create PersonalInformationSign app.PersonalInformationSign = uilabel(app.PersonalTab); app.PersonalInformationSign.HorizontalAlignment = 'center'; app.PersonalInformationSign.FontSize = 26; app.PersonalInformationSign.FontWeight = 'bold'; app.PersonalInformationSign.Position = [1 697 702 46]; app.PersonalInformationSign.Text = 'PERSONAL INFORMATION '; % Create WARNINGBOXLabel app.WARNINGBOXLabel = uilabel(app.PersonalTab); app.WARNINGBOXLabel.HorizontalAlignment = 'center'; app.WARNINGBOXLabel.FontSize = 16; app.WARNINGBOXLabel.FontWeight = 'bold'; app.WARNINGBOXLabel.FontColor = [1 0 0]; app.WARNINGBOXLabel.Position = [489 284 122 22]; app.WARNINGBOXLabel.Text = 'WARNING BOX'; % Create WarningBoxEditField app.WarningBoxEditField = uieditfield(app.PersonalTab, 'text'); app.WarningBoxEditField.HorizontalAlignment = 'center'; app.WarningBoxEditField.FontWeight = 'bold'; app.WarningBoxEditField.Position = [417 103 265 168]; % Create PreviousButton app.PreviousButton = uibutton(app.PersonalTab, 'push'); app.PreviousButton.ButtonPushedFcn = createCallbackFcn(app, @PreviousButtonPushed, true); app.PreviousButton.FontSize = 20; app.PreviousButton.FontWeight = 'bold'; app.PreviousButton.Position = [27 32 134 31]; app.PreviousButton.Text = 'PREVIOUS'; % Create NextButton app.NextButton = uibutton(app.PersonalTab, 'push'); app.NextButton.ButtonPushedFcn = createCallbackFcn(app, @NextButtonPushed, true);

app.NextButton.FontSize = 20; app.NextButton.FontWeight = 'bold'; app.NextButton.Position = [543 32 139 31]; app.NextButton.Text = 'NEXT'; % Create ResetButton app.ResetButton = uibutton(app.PersonalTab, 'push'); app.ResetButton.ButtonPushedFcn = createCallbackFcn(app, @ResetButtonPushed, true); app.ResetButton.FontSize = 20; app.ResetButton.FontWeight = 'bold'; app.ResetButton.Position = [285 32 134 31]; app.ResetButton.Text = 'RESET'; % Create GENDERListBoxLabel app.GENDERListBoxLabel = uilabel(app.PersonalTab); app.GENDERListBoxLabel.HorizontalAlignment = 'center'; app.GENDERListBoxLabel.FontSize = 16; app.GENDERListBoxLabel.FontWeight = 'bold'; app.GENDERListBoxLabel.Position = [27 592 74 22]; app.GENDERListBoxLabel.Text = 'GENDER'; % Create GenderListBox app.GenderListBox = uilistbox(app.PersonalTab); app.GenderListBox.Items = {'Please Choose A Gender', 'Female', 'Male'}; app.GenderListBox.ItemsData = {'', '25', '75'}; app.GenderListBox.ValueChangedFcn = createCallbackFcn(app, @GenderListBoxValueChanged, true); app.GenderListBox.Position = [136 569 205 69]; app.GenderListBox.Value = ''; % Create AGEEditFieldLabel app.AGEEditFieldLabel = uilabel(app.PersonalTab); app.AGEEditFieldLabel.HorizontalAlignment = 'center'; app.AGEEditFieldLabel.FontSize = 16; app.AGEEditFieldLabel.FontWeight = 'bold'; app.AGEEditFieldLabel.Position = [24 508 40 22]; app.AGEEditFieldLabel.Text = 'AGE'; % Create AgeEditField app.AgeEditField = uieditfield(app.PersonalTab, 'numeric'); app.AgeEditField.Limits = [0 Inf]; app.AgeEditField.ValueChangedFcn = createCallbackFcn(app, @AgeEditFieldValueChanged, true); app.AgeEditField.HorizontalAlignment = 'center'; app.AgeEditField.Position = [135 498 206 43]; % Create FAMILYHISTORYLabel app.FAMILYHISTORYLabel = uilabel(app.PersonalTab); app.FAMILYHISTORYLabel.HorizontalAlignment = 'center'; app.FAMILYHISTORYLabel.FontSize = 16; app.FAMILYHISTORYLabel.FontWeight = 'bold'; app.FAMILYHISTORYLabel.Position = [25 396 76 36]; app.FAMILYHISTORYLabel.Text = {'FAMILY '; 'HISTORY'}; % Create FamilyHistoryListBox app.FamilyHistoryListBox = uilistbox(app.PersonalTab);

app.FamilyHistoryListBox.Items = {'Please Choose A Family History', 'No History or Unknown', 'Just Mother', 'Just Father', 'Both Parents', 'Siblings'}; app.FamilyHistoryListBox.ItemsData = {'', '0', '25', '50', '75', '100'}; app.FamilyHistoryListBox.ValueChangedFcn = createCallbackFcn(app, @FamilyHistoryListBoxValueChanged, true); app.FamilyHistoryListBox.Position = [136 358 205 112]; app.FamilyHistoryListBox.Value = ''; % Create NATIONALITYListBoxLabel app.NATIONALITYListBoxLabel = uilabel(app.PersonalTab); app.NATIONALITYListBoxLabel.HorizontalAlignment = 'center'; app.NATIONALITYListBoxLabel.FontSize = 16; app.NATIONALITYListBoxLabel.FontWeight = 'bold'; app.NATIONALITYListBoxLabel.Position = [16 244 112 22]; app.NATIONALITYListBoxLabel.Text = 'NATIONALITY'; % Create NationalityListBox app.NationalityListBox = uilistbox(app.PersonalTab); app.NationalityListBox.Items = {'Please Choose A Nationality', 'North America & Caribbean', 'Middle East and North Africa', 'Europe', 'Western Pacific', 'South and East Asia', 'Africa', 'South and Central America'}; app.NationalityListBox.ItemsData = {'', '0', '17', '33', '50', '67', '83', '100'}; app.NationalityListBox.ValueChangedFcn = createCallbackFcn(app, @NationalityListBoxValueChanged, true); app.NationalityListBox.Position = [136 180 205 149]; app.NationalityListBox.Value = ''; % Create BODYMASSINDEXLabel app.BODYMASSINDEXLabel = uilabel(app.PersonalTab); app.BODYMASSINDEXLabel.HorizontalAlignment = 'center'; app.BODYMASSINDEXLabel.FontSize = 16; app.BODYMASSINDEXLabel.FontWeight = 'bold'; app.BODYMASSINDEXLabel.Position = [16 103 131 44]; app.BODYMASSINDEXLabel.Text = {'BODY MASS'; 'INDEX'}; % Create BodyMassIndexEditField app.BodyMassIndexEditField = uieditfield(app.PersonalTab, 'numeric'); app.BodyMassIndexEditField.Limits = [0 Inf]; app.BodyMassIndexEditField.ValueChangedFcn = createCallbackFcn(app, @BodyMassIndexEditFieldValueChanged, true); app.BodyMassIndexEditField.HorizontalAlignment = 'center'; app.BodyMassIndexEditField.Position = [137 103 205 44]; % Create BiologicalTab app.BiologicalTab = uitab(app.Type2DiagnoseTabGroup); app.BiologicalTab.Title = 'BIOLOGICAL'; % Create BiologicalInformationSign app.BiologicalInformationSign = uilabel(app.BiologicalTab); app.BiologicalInformationSign.HorizontalAlignment = 'center'; app.BiologicalInformationSign.FontSize = 26; app.BiologicalInformationSign.FontWeight = 'bold'; app.BiologicalInformationSign.Position = [1 703 700 41];

app.BiologicalInformationSign.Text = 'LABORATORY & BIOLOGICAL INFORMATION'; % Create BLOODPRESSURELabel app.BLOODPRESSURELabel = uilabel(app.BiologicalTab); app.BLOODPRESSURELabel.HorizontalAlignment = 'center'; app.BLOODPRESSURELabel.FontSize = 18; app.BLOODPRESSURELabel.FontWeight = 'bold'; app.BLOODPRESSURELabel.Position = [24 617 282 39]; app.BLOODPRESSURELabel.Text = 'BLOOD PRESSURE'; % Create CHOLESTEROLLEVELLabel app.CHOLESTEROLLEVELLabel = uilabel(app.BiologicalTab); app.CHOLESTEROLLEVELLabel.HorizontalAlignment = 'center'; app.CHOLESTEROLLEVELLabel.FontSize = 18; app.CHOLESTEROLLEVELLabel.FontWeight = 'bold'; app.CHOLESTEROLLEVELLabel.Position = [401 470 282 34]; app.CHOLESTEROLLEVELLabel.Text = 'CHOLESTEROL LEVEL'; % Create BLOODGLUCOSELEVELLabel app.BLOODGLUCOSELEVELLabel = uilabel(app.BiologicalTab); app.BLOODGLUCOSELEVELLabel.HorizontalAlignment = 'center'; app.BLOODGLUCOSELEVELLabel.FontSize = 18; app.BLOODGLUCOSELEVELLabel.FontWeight = 'bold'; app.BLOODGLUCOSELEVELLabel.Position = [401 617 282 39]; app.BLOODGLUCOSELEVELLabel.Text = 'BLOOD GLUCOSE LEVEL'; % Create WARNINGBOXEditFieldLabel app.WARNINGBOXEditFieldLabel = uilabel(app.BiologicalTab); app.WARNINGBOXEditFieldLabel.HorizontalAlignment = 'center'; app.WARNINGBOXEditFieldLabel.FontSize = 16; app.WARNINGBOXEditFieldLabel.FontWeight = 'bold'; app.WARNINGBOXEditFieldLabel.FontColor = [1 0 0]; app.WARNINGBOXEditFieldLabel.Position = [401 318 282 22]; app.WARNINGBOXEditFieldLabel.Text = 'WARNING BOX'; % Create WarningBoxEditField2 app.WarningBoxEditField2 = uieditfield(app.BiologicalTab, 'text'); app.WarningBoxEditField2.Position = [405 174 278 145]; % Create PreviousButton_2 app.PreviousButton_2 = uibutton(app.BiologicalTab, 'push'); app.PreviousButton_2.ButtonPushedFcn = createCallbackFcn(app, @PreviousButton_2Pushed, true); app.PreviousButton_2.FontSize = 20; app.PreviousButton_2.FontWeight = 'bold'; app.PreviousButton_2.Position = [24 50 134 31]; app.PreviousButton_2.Text = 'PREVIOUS'; % Create NextButton_2 app.NextButton_2 = uibutton(app.BiologicalTab, 'push'); app.NextButton_2.ButtonPushedFcn = createCallbackFcn(app, @NextButton_2Pushed, true); app.NextButton_2.FontSize = 20; app.NextButton_2.FontWeight = 'bold'; app.NextButton_2.Position = [544 50 139 31]; app.NextButton_2.Text = 'NEXT'; % Create ResetButton_2 app.ResetButton_2 = uibutton(app.BiologicalTab, 'push');

app.ResetButton_2.ButtonPushedFcn = createCallbackFcn(app, @ResetButton_2Pushed, true); app.ResetButton_2.FontSize = 20; app.ResetButton_2.FontWeight = 'bold'; app.ResetButton_2.Position = [305 50 134 31]; app.ResetButton_2.Text = 'RESET'; % Create SYSTOLICBLOODPRESSUREEditFieldLabel app.SYSTOLICBLOODPRESSUREEditFieldLabel = uilabel(app.BiologicalTab); app.SYSTOLICBLOODPRESSUREEditFieldLabel.HorizontalAlignment = 'center'; app.SYSTOLICBLOODPRESSUREEditFieldLabel.FontSize = 16; app.SYSTOLICBLOODPRESSUREEditFieldLabel.FontWeight = 'bold'; app.SYSTOLICBLOODPRESSUREEditFieldLabel.Position = [7 522 94 54]; app.SYSTOLICBLOODPRESSUREEditFieldLabel.Text = {'SYSTOLIC'; 'BLOOD'; 'PRESSURE'}; % Create SystolicBloodPressureEditField app.SystolicBloodPressureEditField = uieditfield(app.BiologicalTab, 'numeric'); app.SystolicBloodPressureEditField.ValueChangedFcn = createCallbackFcn(app, @SystolicBloodPressureEditFieldValueChanged, true); app.SystolicBloodPressureEditField.HorizontalAlignment = 'center'; app.SystolicBloodPressureEditField.Position = [116 512 173 74]; % Create DIASTOLICBLOODPRESSUREEditFieldLabel app.DIASTOLICBLOODPRESSUREEditFieldLabel = uilabel(app.BiologicalTab); app.DIASTOLICBLOODPRESSUREEditFieldLabel.HorizontalAlignment = 'center'; app.DIASTOLICBLOODPRESSUREEditFieldLabel.FontSize = 16; app.DIASTOLICBLOODPRESSUREEditFieldLabel.FontWeight = 'bold'; app.DIASTOLICBLOODPRESSUREEditFieldLabel.Position = [7 396 94 57]; app.DIASTOLICBLOODPRESSUREEditFieldLabel.Text = {'DIASTOLIC'; 'BLOOD'; 'PRESSURE'}; % Create DiastolicBloodPressureEditField app.DiastolicBloodPressureEditField = uieditfield(app.BiologicalTab, 'numeric'); app.DiastolicBloodPressureEditField.ValueChangedFcn = createCallbackFcn(app, @DiastolicBloodPressureEditFieldValueChanged, true); app.DiastolicBloodPressureEditField.HorizontalAlignment = 'center'; app.DiastolicBloodPressureEditField.Position = [117 391 172 70]; % Create CHOLESTEROLLEVELLabel_2 app.CHOLESTEROLLEVELLabel_2 = uilabel(app.BiologicalTab); app.CHOLESTEROLLEVELLabel_2.HorizontalAlignment = 'center'; app.CHOLESTEROLLEVELLabel_2.FontSize = 16; app.CHOLESTEROLLEVELLabel_2.FontWeight = 'bold'; app.CHOLESTEROLLEVELLabel_2.Position = [384 392 126 70]; app.CHOLESTEROLLEVELLabel_2.Text = {'CHOLESTEROL'; 'LEVEL'}; % Create CholesterolEditField app.CholesterolEditField = uieditfield(app.BiologicalTab, 'numeric');

app.CholesterolEditField.ValueChangedFcn = createCallbackFcn(app, @CholesterolEditFieldValueChanged, true); app.CholesterolEditField.HorizontalAlignment = 'center'; app.CholesterolEditField.Position = [517 391 166 72]; % Create BLOODGLUCOSELEVELEditFieldLabel app.BLOODGLUCOSELEVELEditFieldLabel = uilabel(app.BiologicalTab); app.BLOODGLUCOSELEVELEditFieldLabel.HorizontalAlignment = 'center'; app.BLOODGLUCOSELEVELEditFieldLabel.FontSize = 16; app.BLOODGLUCOSELEVELEditFieldLabel.FontWeight = 'bold'; app.BLOODGLUCOSELEVELEditFieldLabel.Position = [405 522 85 74]; app.BLOODGLUCOSELEVELEditFieldLabel.Text = {'BLOOD'; 'GLUCOSE'; 'LEVEL'}; % Create BloodGlucoseLevelEditField app.BloodGlucoseLevelEditField = uieditfield(app.BiologicalTab, 'numeric'); app.BloodGlucoseLevelEditField.ValueChangedFcn = createCallbackFcn(app, @BloodGlucoseLevelEditFieldValueChanged, true); app.BloodGlucoseLevelEditField.HorizontalAlignment = 'center'; app.BloodGlucoseLevelEditField.Position = [517 522 166 74]; % Create PREGNANCYSITUATIONListBoxLabel app.PREGNANCYSITUATIONListBoxLabel = uilabel(app.BiologicalTab); app.PREGNANCYSITUATIONListBoxLabel.HorizontalAlignment = 'center'; app.PREGNANCYSITUATIONListBoxLabel.FontSize = 16; app.PREGNANCYSITUATIONListBoxLabel.FontWeight = 'bold'; app.PREGNANCYSITUATIONListBoxLabel.Position = [7 229 108 36]; app.PREGNANCYSITUATIONListBoxLabel.Text = {'PREGNANCY'; 'SITUATION'}; % Create PregnancySituationListBox app.PregnancySituationListBox = uilistbox(app.BiologicalTab); app.PregnancySituationListBox.Items = {'Please Choose A Pregnancy Situation', 'Unknown Information', 'Positive', 'Negative'}; app.PregnancySituationListBox.ItemsData = {'', '0', '100', '0'}; app.PregnancySituationListBox.ValueChangedFcn = createCallbackFcn(app, @PregnancySituationListBoxValueChanged, true); app.PregnancySituationListBox.Position = [134 174 233 145]; app.PregnancySituationListBox.Value = ''; % Create QuestionTab app.QuestionTab = uitab(app.Type2DiagnoseTabGroup); app.QuestionTab.Title = 'QUESTION'; % Create INFORMATIONCHOOSINGLabel app.INFORMATIONCHOOSINGLabel = uilabel(app.QuestionTab); app.INFORMATIONCHOOSINGLabel.HorizontalAlignment = 'center'; app.INFORMATIONCHOOSINGLabel.FontSize = 26; app.INFORMATIONCHOOSINGLabel.FontWeight = 'bold'; app.INFORMATIONCHOOSINGLabel.Position = [1 696 700 33]; app.INFORMATIONCHOOSINGLabel.Text = 'INFORMATION CHOOSING'; % Create TextArea app.TextArea = uitextarea(app.QuestionTab); app.TextArea.FontSize = 16; app.TextArea.Position = [3 416 698 170];

app.TextArea.Value = {''; 'If you know at least one of the patient''s lifestyle habit information (dietary factors, physical activity level, smoking habit or alcohol consumption), please click the ''YES'' button and continue the lifestyle screen!'; ''; 'If you do not know any of the patient''s lifestyle habit information, please click the ''NO'' button and continue the diagnose screen!'}; % Create YesButton app.YesButton = uibutton(app.QuestionTab, 'push'); app.YesButton.ButtonPushedFcn = createCallbackFcn(app, @YesButtonPushed, true); app.YesButton.FontSize = 18; app.YesButton.FontWeight = 'bold'; app.YesButton.Position = [38 253 151 60]; app.YesButton.Text = 'YES'; % Create NoButton app.NoButton = uibutton(app.QuestionTab, 'push'); app.NoButton.ButtonPushedFcn = createCallbackFcn(app, @NoButtonPushed, true); app.NoButton.FontSize = 18; app.NoButton.FontWeight = 'bold'; app.NoButton.Position = [537 253 151 60]; app.NoButton.Text = 'NO'; % Create PreviousButton_3 app.PreviousButton_3 = uibutton(app.QuestionTab, 'push'); app.PreviousButton_3.ButtonPushedFcn = createCallbackFcn(app, @PreviousButton_3Pushed, true); app.PreviousButton_3.FontSize = 20; app.PreviousButton_3.FontWeight = 'bold'; app.PreviousButton_3.Position = [38 33 134 31]; app.PreviousButton_3.Text = 'PREVIOUS'; % Create NextButton_3 app.NextButton_3 = uibutton(app.QuestionTab, 'push'); app.NextButton_3.ButtonPushedFcn = createCallbackFcn(app, @NextButton_3Pushed, true); app.NextButton_3.FontSize = 20; app.NextButton_3.FontWeight = 'bold'; app.NextButton_3.Position = [549 33 139 31]; app.NextButton_3.Text = 'NEXT'; % Create LifestyleTab app.LifestyleTab = uitab(app.Type2DiagnoseTabGroup); app.LifestyleTab.Title = 'LIFESTYLE'; % Create LIFESTYLEINFORMATIONLabel app.LIFESTYLEINFORMATIONLabel = uilabel(app.LifestyleTab); app.LIFESTYLEINFORMATIONLabel.HorizontalAlignment = 'center'; app.LIFESTYLEINFORMATIONLabel.FontSize = 26; app.LIFESTYLEINFORMATIONLabel.FontWeight = 'bold'; app.LIFESTYLEINFORMATIONLabel.Position = [5 697 695 59]; app.LIFESTYLEINFORMATIONLabel.Text = 'LIFESTYLE INFORMATION'; % Create PreviousButton_4 app.PreviousButton_4 = uibutton(app.LifestyleTab, 'push'); app.PreviousButton_4.ButtonPushedFcn = createCallbackFcn(app, @PreviousButton_4Pushed, true);

app.PreviousButton_4.FontSize = 20; app.PreviousButton_4.FontWeight = 'bold'; app.PreviousButton_4.Position = [40 34 128 31]; app.PreviousButton_4.Text = 'PREVIOUS'; % Create NextButton_4 app.NextButton_4 = uibutton(app.LifestyleTab, 'push'); app.NextButton_4.ButtonPushedFcn = createCallbackFcn(app, @NextButton_4Pushed, true); app.NextButton_4.FontSize = 20; app.NextButton_4.FontWeight = 'bold'; app.NextButton_4.Position = [541 34 133 31]; app.NextButton_4.Text = 'NEXT'; % Create ResetButton_3 app.ResetButton_3 = uibutton(app.LifestyleTab, 'push'); app.ResetButton_3.ButtonPushedFcn = createCallbackFcn(app, @ResetButton_3Pushed, true); app.ResetButton_3.FontSize = 20; app.ResetButton_3.FontWeight = 'bold'; app.ResetButton_3.Position = [290 34 128 31]; app.ResetButton_3.Text = 'RESET'; % Create SMOKINGHABITListBoxLabel app.SMOKINGHABITListBoxLabel = uilabel(app.LifestyleTab); app.SMOKINGHABITListBoxLabel.HorizontalAlignment = 'center'; app.SMOKINGHABITListBoxLabel.FontSize = 16; app.SMOKINGHABITListBoxLabel.FontWeight = 'bold'; app.SMOKINGHABITListBoxLabel.Position = [86 275 82 36]; app.SMOKINGHABITListBoxLabel.Text = {'SMOKING'; 'HABIT'}; % Create SmokingHabitListBox app.SmokingHabitListBox = uilistbox(app.LifestyleTab); app.SmokingHabitListBox.Items = {'Please Choose A Smoking Habit', 'Unknown Information', 'Never Smoke', 'Passive or Second-Hand Smoker', 'Used to be Smoker (Quited Smoke)', 'Active Smoker (1 or more)'}; app.SmokingHabitListBox.ItemsData = {'', '1', '0', '33.33', '66.76', '100'}; app.SmokingHabitListBox.ValueChangedFcn = createCallbackFcn(app, @SmokingHabitListBoxValueChanged, true); app.SmokingHabitListBox.Position = [217 237 418 113]; app.SmokingHabitListBox.Value = ''; % Create ALCOHOLCONSUMPTIONListBoxLabel app.ALCOHOLCONSUMPTIONListBoxLabel = uilabel(app.LifestyleTab); app.ALCOHOLCONSUMPTIONListBoxLabel.HorizontalAlignment = 'center'; app.ALCOHOLCONSUMPTIONListBoxLabel.FontSize = 16; app.ALCOHOLCONSUMPTIONListBoxLabel.FontWeight = 'bold'; app.ALCOHOLCONSUMPTIONListBoxLabel.Position = [80 141 125 36]; app.ALCOHOLCONSUMPTIONListBoxLabel.Text = {'ALCOHOL'; 'CONSUMPTION'}; % Create AlcoholConsumptionListBox app.AlcoholConsumptionListBox = uilistbox(app.LifestyleTab); app.AlcoholConsumptionListBox.Items = {'Please Choose An Alcohol Consumption', 'Unknown Information', 'Never Drink', 'Low Consumption or Social Drinker', 'Regular Drinker', 'Addicted (Every Moment)'};

app.AlcoholConsumptionListBox.ItemsData = {'', '1', '50', '0', '33.33', '66.48', '100'}; app.AlcoholConsumptionListBox.ValueChangedFcn = createCallbackFcn(app, @AlcoholConsumptionListBoxValueChanged, true); app.AlcoholConsumptionListBox.Position = [217 104 418 111]; app.AlcoholConsumptionListBox.Value = ''; % Create EatingHabitListBox app.EatingHabitListBox = uilistbox(app.LifestyleTab); app.EatingHabitListBox.Items = {'Please Choose An Eating Habit Style', 'Unknown Information', 'Unhealthier', 'Partial Healthier', 'Healthier'}; app.EatingHabitListBox.ItemsData = {'', '1', '50', '0', '50', '100'}; app.EatingHabitListBox.ValueChangedFcn = createCallbackFcn(app, @EatingHabitListBoxValueChanged, true); app.EatingHabitListBox.Position = [217 376 418 113]; app.EatingHabitListBox.Value = ''; % Create EATINGHABITORDIETARYRESTRICTIONLabel app.EATINGHABITORDIETARYRESTRICTIONLabel = uilabel(app.LifestyleTab); app.EATINGHABITORDIETARYRESTRICTIONLabel.HorizontalAlignment = 'center'; app.EATINGHABITORDIETARYRESTRICTIONLabel.FontSize = 16; app.EATINGHABITORDIETARYRESTRICTIONLabel.FontWeight = 'bold'; app.EATINGHABITORDIETARYRESTRICTIONLabel.Position = [70 387 114 90]; app.EATINGHABITORDIETARYRESTRICTIONLabel.Text = {'EATING'; 'HABIT'; 'OR '; 'DIETARY'; 'RESTRICTION'}; % Create PhysicalActivityLevelListBox app.PhysicalActivityLevelListBox = uilistbox(app.LifestyleTab); app.PhysicalActivityLevelListBox.Items = {'Please Choose A Physical Activity Level', 'Unknown Information', 'Sedentary', 'Low Active', 'Active', 'Very Active'}; app.PhysicalActivityLevelListBox.ItemsData = {'', '1', '50', '0', '33.33', '66.67', '100'}; app.PhysicalActivityLevelListBox.ValueChangedFcn = createCallbackFcn(app, @PhysicalActivityLevelListBoxValueChanged, true); app.PhysicalActivityLevelListBox.Position = [217 516 418 141]; app.PhysicalActivityLevelListBox.Value = ''; % Create PHYSICALACTIVITYLEVELLabel app.PHYSICALACTIVITYLEVELLabel = uilabel(app.LifestyleTab); app.PHYSICALACTIVITYLEVELLabel.HorizontalAlignment = 'center'; app.PHYSICALACTIVITYLEVELLabel.FontSize = 16; app.PHYSICALACTIVITYLEVELLabel.FontWeight = 'bold'; app.PHYSICALACTIVITYLEVELLabel.Position = [84 559 86 54]; app.PHYSICALACTIVITYLEVELLabel.Text = {'PHYSICAL'; 'ACTIVITY'; 'LEVEL'}; % Create DiagnoseTab app.DiagnoseTab = uitab(app.Type2DiagnoseTabGroup); app.DiagnoseTab.Title = 'DIAGNOSE'; % Create RESULTSLabel app.RESULTSLabel = uilabel(app.DiagnoseTab);

app.RESULTSLabel.HorizontalAlignment = 'center'; app.RESULTSLabel.FontSize = 26; app.RESULTSLabel.FontWeight = 'bold'; app.RESULTSLabel.Position = [1 709 702 47]; app.RESULTSLabel.Text = 'RESULTS'; % Create DiagnoseButton app.DiagnoseButton = uibutton(app.DiagnoseTab, 'push'); app.DiagnoseButton.ButtonPushedFcn = createCallbackFcn(app, @DiagnoseButtonPushed, true); app.DiagnoseButton.FontSize = 20; app.DiagnoseButton.FontWeight = 'bold'; app.DiagnoseButton.Position = [285 33 134 31]; app.DiagnoseButton.Text = 'DIAGNOSE'; % Create CloseButton app.CloseButton = uibutton(app.DiagnoseTab, 'push'); app.CloseButton.ButtonPushedFcn = createCallbackFcn(app, @CloseButtonPushed, true); app.CloseButton.FontSize = 20; app.CloseButton.FontWeight = 'bold'; app.CloseButton.Position = [544 19 150 31]; app.CloseButton.Text = 'CLOSE'; % Create NewPatientButton app.NewPatientButton = uibutton(app.DiagnoseTab, 'push'); app.NewPatientButton.ButtonPushedFcn = createCallbackFcn(app, @NewPatientButtonPushed, true); app.NewPatientButton.FontSize = 20; app.NewPatientButton.FontWeight = 'bold'; app.NewPatientButton.Position = [544 49 150 31]; app.NewPatientButton.Text = 'NEW PATIENT'; % Create PERSONALFEATURESLabel app.PERSONALFEATURESLabel = uilabel(app.DiagnoseTab); app.PERSONALFEATURESLabel.HorizontalAlignment = 'center'; app.PERSONALFEATURESLabel.FontSize = 14; app.PERSONALFEATURESLabel.FontWeight = 'bold'; app.PERSONALFEATURESLabel.Position = [25 583 83 32]; app.PERSONALFEATURESLabel.Text = {'PERSONAL'; 'FEATURES'}; % Create PersonalEditField app.PersonalEditField = uieditfield(app.DiagnoseTab, 'text'); app.PersonalEditField.HorizontalAlignment = 'center'; app.PersonalEditField.FontSize = 14; app.PersonalEditField.Position = [143 532 551 121]; % Create BIOLOGICALFEATURESLabel app.BIOLOGICALFEATURESLabel = uilabel(app.DiagnoseTab); app.BIOLOGICALFEATURESLabel.HorizontalAlignment = 'center'; app.BIOLOGICALFEATURESLabel.FontSize = 14; app.BIOLOGICALFEATURESLabel.FontWeight = 'bold'; app.BIOLOGICALFEATURESLabel.Position = [20 439 93 32]; app.BIOLOGICALFEATURESLabel.Text = {'BIOLOGICAL'; 'FEATURES'}; % Create BiologicalEditField app.BiologicalEditField = uieditfield(app.DiagnoseTab, 'text'); app.BiologicalEditField.HorizontalAlignment = 'center'; app.BiologicalEditField.FontSize = 14;

app.BiologicalEditField.Position = [143 388 551 121]; % Create LIFESTYLEFEATURESLabel app.LIFESTYLEFEATURESLabel = uilabel(app.DiagnoseTab); app.LIFESTYLEFEATURESLabel.HorizontalAlignment = 'center'; app.LIFESTYLEFEATURESLabel.FontSize = 14; app.LIFESTYLEFEATURESLabel.FontWeight = 'bold'; app.LIFESTYLEFEATURESLabel.Position = [26 296 81 32]; app.LIFESTYLEFEATURESLabel.Text = {'LIFESTYLE'; 'FEATURES'}; % Create LifeStyleEditField app.LifeStyleEditField = uieditfield(app.DiagnoseTab, 'text'); app.LifeStyleEditField.HorizontalAlignment = 'center'; app.LifeStyleEditField.FontSize = 14; app.LifeStyleEditField.Position = [143 245 551 121]; % Create RESULTSLabel_2 app.RESULTSLabel_2 = uilabel(app.DiagnoseTab); app.RESULTSLabel_2.HorizontalAlignment = 'center'; app.RESULTSLabel_2.FontSize = 14; app.RESULTSLabel_2.FontWeight = 'bold'; app.RESULTSLabel_2.Position = [32 162 70 22]; app.RESULTSLabel_2.Text = 'RESULTS'; % Create ResultsEditField app.ResultsEditField = uieditfield(app.DiagnoseTab, 'text'); app.ResultsEditField.HorizontalAlignment = 'center'; app.ResultsEditField.FontSize = 14; app.ResultsEditField.FontWeight = 'bold'; app.ResultsEditField.Position = [143 101 551 121]; end end methods (Access = public) % Construct app function app = LastUpdatedFebruaryType2DiabetesMellitusDiagnosis % Create and configure components createComponents(app) % Register the app with App Designer registerApp(app, app.UIFigure) if nargout == 0 clear app end end % Code that executes before app deletion function delete(app) % Delete UIFigure when app is deleted delete(app.UIFigure) end end end

AN INTELLIGENT SYSTEM FOR TYPE II DIABETES ...

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