An Efficient Framework for Prediction in Healthcare

Data using Soft Computing Techniques

Veena H Bhat1,2, Krishna S1, Prasanth G Rao

3, P Deepa Shenoy1,

Venugopal K R1, L M Patnaik

4,

1 Department of Computer Science and Engineering, University Visvesvaraya College of

Engineering, Bangalore, India 2 IBS-Bangalore, Bangalore, India

3Research Scholar, Bangalore, India 4 Vice Chancellor, Defense Institute of Advanced Technology, Pune, India

{veena.h.bhat, krishna.somandepalli, prasanthgrao}@gmail.com, shenoypd@yahoo.com

Abstract. Healthcare organizations aim at deriving valuable insights employing

data mining and soft computing techniques on the vast data stores that have

been accumulated over the years. This data however, might consist of missing,

incorrect and most of the time, incomplete instances that can have a detrimental

effect on the predictive analytics of the healthcare data. Preprocessing of this

data, specifically the imputation of missing values offers a challenge for reliable

modeling. This work presents a novel preprocessing phase with missing value

imputation for both numerical and categorical data. A hybrid combination of

Classification and Regression Trees (CART) and Genetic Algorithms to impute

missing continuous values and Self Organizing Feature Maps (SOFM) to

impute categorical values is adapted in this work. Further, Artificial Neural

Networks (ANN) is used to validate the improved accuracy of prediction after

imputation. To evaluate this model, we use PIMA Indians Diabetes Data set

(PIDD), and Mammographic Mass Data (MMD). The accuracy of the proposed

model that emphasizes on a preprocessing phase is shown to be superior over

the existing techniques. This approach is simple, easy to implement and

practically reliable.

Keywords: Imputation, soft computing, categorical data, continuous data.

1 Introduction

Quality of the data under study plays a pivotal role in the accuracy of modeling using

data mining techniques and predictive analytics. The objective of data mining is to

find patterns in the data that gives accurate outcomes with newer, unseen cases. In

healthcare databases, a huge number of recorded samples are used to analyze

characteristic hidden patterns; the validity of the model is verified by comparing the

outcome of the model for an unseen case and its corresponding medical expert

opinion.

In the real world, data has a cost attached to it, especially in the field of medical

diagnostics. The medical experiments conducted to record data are expensive, often

repetitive and invasive in nature, which sometimes might increase the threat on the

patient’s life, hence not repeatable. Therefore it is of utmost importance to have

reliable and cost efficient methods to reuse data and give an opinion to the patient

without much medical re-experimentation but using simple software that holds the

simulated statistical model.

Cabena et al., [1] in their work estimate that about 20% of the efforts are spent on

problem identification, about 60% on data preparation and data preprocessing while

the rest about 20% for data mining and knowledge discovery; which definitely

upholds the importance of data preprocessing. Missing values or missing data in any

database, especially in a healthcare data set is often an indelible hurdle for efficient

analyses. Such missing values can occur due to non response or a simple missed

entry. This reduces the representativeness of the sample thus distorting the inferences

drawn. The presence of missing values at rates less than 1% are generally considered

trivial, 1-5% manageable, 5-15% require sophisticated methods to handle the missing

values and more than 15% severely affect any kind of interpretation [2].

Soft computing techniques aim at improving quality of data by reducing

imprecision and uncertainty employing approximate reasoning and logic, in order to

achieve tractability, robustness and low cost solutions. Popular methods include

neural networks, swarm intelligence, rough sets, fuzzy logic and genetic algorithms.

This work proposes a novel modeling methodology, specifically for healthcare data

which is chosen so as to contain continuous, categorical, numeric alongside textual

data as variables in the several attributes considered. The data is chosen so as include

instances of missing values which are imputed using separate approaches for

continuous and categorical data. The proposed methodology for imputation is

validated for prediction accuracy, using Artificial Neural Networks (ANN) as the

classifier. Results show considerable improvement in the accuracy of the proposed

model as against other techniques in vogue.

Section 2 outlines the related work. The motivation to carry out this work is

explained in section 3. To make this paper self-contained we have described the data

set details in section 4. The framework proposed for data preprocessing is explained

in detail in section 5. Section 6 describes the implementation of the entire model – the

preprocessing framework along with the prediction model. Results and performance

analyses are described in section 7. Conclusions and future work is given in section 8.

2 Literature Survey

Reference [3] details the three rules any effective imputation model should follow –

retaining the data distribution, the relationship between the attributes and cost-time

efficiency. Some of the predominant methods for imputation include Mean

Imputation (MI), Regression Imputation, C4.5 and k-Nearest Neighbor (k-NN) [1, 4,

5, 6, 14]. The PIMA data set has been explored by researchers, applying many data

mining techniques. In the previous works adopting neural networks [7,8,9] and other

classifiers [12, 13, 15] in the prediction modeling stage, the accuracy in predicting the

diabetes status ranges from 66% to 82.29%. Works related with Mammographic Mass

Data (MMD) have reported prediction accuracy upto 80.9% [10]. Most of the

research works either work on complete instances or ignore the tuples with attribute

values set to zero [7, 8] while [9] considers data instances of PIDD (to predict

continuous values) with an equal mix of patients who had diabetes onset and patients

who were non-diabetic, but with attributes that contained missing/incorrect values.

In this work, the data sets selected have missing value range (incorrect value) of

4% to 48%. The proposed framework is aimed at achieving a cost effective

imputation method to assess the accuracy of the imputed values at each iteration. A

prediction model is then constructed which works on the corrected data, which now

neither has missing values or incorrect values. Further the accuracy of the model is

established and analyzed using various parameters as explained in section 7.

3 Motivation

Today, healthcare information systems amass a large volume of digital data in various

forms, but the analyses of such data for preventive diagnosis; prediction of disease

transmission and epidemic outbreaks is not frequent. Analyzing healthcare data for an

efficient patient-care and tracking the patients’ records that include critical parameters

such as medicine intake or dosage may give a peek into the long term effects that

could be caused by a particular diagnostic tool or treatment. An intelligent, robust and

reliable prediction model could help in the identification of this kind of analyses and

predicting long-term probable health issues. This could also help the doctors or other

health coordinators who oversee the treatment of a certain patient to give quality

diagnosis and help identify the changes in the lifestyle that may be necessary in the

patient’s routine to prevent short and long term health complexities caused by an

illness. Though several models for such prediction and analyses are in vogue, a robust

and reliable model that predicts with a good accuracy even when the attributes include

missing and erroneous values is a challenge. In this work we propose a solution to

such analyses by employing various soft computing techniques in preprocessing the

data to impute both categorical and numerical missing values that further improves

accuracy of the prediction.

4 Data Set Details

The data sets selected for building the predictive model are PIMA Indian Diabetes

Data (PIDD) set and the Mammographic Mass Data (MMD) from the Machine

Learning Database Repository at the University of California, Irvine. These data sets

may be downloaded from [11].

As per previous studies, Pima Indians may be genetically predisposed to diabetes

and it was noted that their diabetic rate was 19 times that of any typical town. The

National Institute of Diabetes and Digestive and Kidney Diseases of the NIH

originally owned the PIDD and was received by UC-Irvine Machine Learning

Repository in 1990. The data set contains 768 patient data records and each record is

described by 8 attributes, while the 9th attribute associates the class attribute that is a

label indicating the onset of diabetes within 5 years. A ‘0’ indicates the onset and ‘1’

indicates non-occurrence of diabetes. This data constitutes of female patients of age

21 years and above belonging to the Pima Indian heritage. This population lives near

Phoenix, Arizona, USA.

(a) (b)

Fig. 1. Boxplots of the PIDD attribute values before and after soft computing preprocessing.

The Mammographic Mass Data (MMD) was received by UC-Irvine Machine

Learning Repository in 2007. This data set contains the Breast Imaging–Reporting

and Data Systems (BI-RADS) assessment and the patient’s age related data for 916

instances that have been identified on full field digital mammograms collected at the

Institute of Radiology of the University Erlangen-Nuremberg between 2003 and 2006.

The data is categorical in nature with BI-RADS assessment ranging from 1 (definitely

benign) to 5 (highly suggestive malignancy) assigned in a double-review process by

physicians. The data has a total of 5 attributes with the 6th attribute being the severity

(benign = 0, malignant = 1). The age of the patient is an integer (maximum value

being 96 and minimum 18), the shape of the mass takes values ranging from 1 to 4

(where round=1 oval=2 lobular=3 irregular=4), the mass margin takes values from 1

to 5 (where circumscribed=1 microlobulated=2 obscured=3 ill-defined=4

spiculated=5) and mass density being ordinal, takes values from 1 to 4 (high=1 iso=2

low=3 fat-containing=4).

Data sets are usually explored either graphically or analytically. The quality of data

being explored is an important aspect considering presence of missing and/or outlier

values. These affect the pattern recognition and predictive analyses of the data being

mined. Several methods are used to handle the noisy and missing data like replacing

the incorrect values with ‘mean’ of the variables in the attribute, its ‘median’ or

sometimes user defined values. The visualization methodology adopted in this work

for data exploration is illustrated in Fig. 1(a) employing boxplots that are obtained

from PIDD. This boxplot indicates the quartiles, median besides the outlier values for

each attribute.

The Pima Indian Diabetes Data (PIDD) set when examined manually, is observed

to contain few incorrect values for specific attributes, as listed in Table 1. PIDD

includes 500 non-diabetic patients (class = negative; ‘0’) and 268 diabetic patients

(class = positive; ‘1’) giving an incidence rate of 34.9%. Thus if a random classifier is

used for prediction for all instances as belonging to negative class (that is all instances

being predicted negative) then accuracy of such a model would be 65.1% (or an error

rate of 34.9%). Using this simple strategy of selecting tuples that contain complete

and correct data values, we statistically represent the data selected, using the boxplot

to aid analysis of the data.

Table 1. Attribute-value study outcome, indicating attributes with incorrectly present values

for PIDD set.

Sl. No. Attribute Name

No. of `Zero’ value

instances for attributes

listed, present out of 768

tuples.

1 Plasma glucose concentration, in 2 hours, in

an oral glucose tolerance test

5

2 Diastolic blood pressure (mm Hg) 35

3 Triceps skin fold thickness (mm) 227

4 2-hour serum insulin (mu U/ml) 374

5 Body mass index (weight in kg/(height in

m)2)

11

Mammographic Mass Data (MMD), consists of 916 instances, that include 516

instances of benign (class = negative; ‘0’) and 445 instances of malignant masses

(class = positive; ‘1’), with an incidence rate of 48.58%. In this particular data set,

the attribute that requires imputation is ‘density’ as this attribute shows the highest

number of missing or erroneous value shown in Table 2.

Table 2. Attribute-value study outcome, indicating attributes with incorrectly present values for

MMD.

Sl.No. Attribute Name

No. of `-1’ value

instances for attributes

listed, present out of

961 tuples.

1. BI-RADS assessment 2

2. Age 5

3. Shape 31

4. Margin 48

5. Density 76

5 Proposed Data Preprocessing Framework

The data imputation framework provides a reliable solution to both continuous and

categorical missing values. This makes the proposed model unique and self-

contained. PIDD data is used to demonstrate the imputing of continuous values where

as MMD set is identified to impute categorical values.

5.1 Proposed approach to impute continuous missing values

A regression model, using Classification and Regression Trees (CART), is

developed, using tuples that have complete and correct values for all its attributes.

These tuples constitute the training set for the model. Further, the instances that would

need values to be imputed are input to this model. The set of outliers of the original

data is compared with the set of generated outlier values.

A second order quadratic error function is generated. Genetic Algorithms is used to

optimize the error function in any given domain. Using a Genetic Algorithm function

call, we find a local unconstrained minimum, x, to the objective function, fitnessfcn.

x = ga(fitnessfcn, nvars) (1)

nvars is the dimension (number of design variables) of fitnessfcn. The objective

function, fitnessfcn, accepts a vector x of size 1-by-nvars, and returns a scalar

evaluated at x. The predicted value for the incorrect or missing value is corrected

using this optimization through genetic algorithms. The proposed module to impute

categorical values is illustrated in Fig. 2.

Fig. 2. Module to impute continuous values.

PIDD set contains missing/incorrect value ranging from 4% (taking only the

records or instance containing zero values to the attribute –‘diastolic blood pressure’)

to 48% (taking only the records or instances containing zero values to the attribute –

‘2 hour serum insulin presence’). This work implements the proposed data

preprocessing framework, to impute continuous values, for the attribute ‘2-hour

serum insulin’ (with 48% incorrect values). A box-plot is once again employed to

inspect the mean and the outliers in the data, for any further improvement shown as

shown in Fig 1(b). The attribute ‘2-hour serum insulin’ is represented by column five

Select data-subset

without any

missing values

out

Build regression

model to impute

missing values

Compute error

function for

predicted values

Optimize error

function using

Genetic Algorithm

Impute value

correction

Completed

data set, imputing

continuous values

in the boxplots which indicates the differences before and after the imputation

process.

The now ‘complete’ data set (with respect to the attribute ‘2 hour serum insulin

presence’) with an optimized error is ready to be used for predictive analytics.

5.2 Proposed approach to impute categorical missing values

With the exception of the attribute ‘age’ (continuous), the nature of all other

attributes of MMD is categorical. Hence a model that identifies and detects the

regularities and correlations between these input functions is one of the crucial steps

of this framework.

Fig. 3. Module to impute categorical values.

The technique of Self Organizing Feature Maps (SOFM) is selected to model the

complete instances of the MMD, as the neurons of the competitive network learn to

recognize the groups of similar vector inputs. SOFM is trained using the complete

tuples. Learning Vector Quantization (LVQ) method is used for supervised training of

the competitive layers. The instances that include erroneous values are input to the

thus simulated model, for classification. The co-ordinates of the classification,

deduced from the simulated SOFM, is used to map the value to be imputed, using the

mode of the values. The proposed module to impute categorical values is illustrated in

Fig. 3.

The imputation of categorical data was experimented using MMD, wherein the

data set contains missing/erroneous value ranging from 0.2% to 7.9%. The attribute

selected from imputation is ‘density of the mass’, which had the highest error

percentage in the data of 7.9%. The MMD data is input to the proposed framework to

impute categorical values, using the SOFM concept.

The completed data set where the data was imputed for the attribute ‘density of the

mass’ alone, is now ready to be tested on the prediction model, where multi-layer

perceptron neural network is selected as the classifier.

Select data-subset

without any

missing values

out

Build SOFM

model to impute

missing values

Classify the

instances with

incorrect values

using the SOFM

model

Get the

coordinates of the

classified

instances

Impute value

correction using

the mode

Completed

data set, imputing

categorical values

6 Prediction Model

The architecture of the proposed model is depicted in Fig. 4. Artificial Neural

Networks (ANN), a supervised learning data mining approach, is selected as the

classifier as it is sensitive to non-linear input values and high on prediction efficiency.

The preprocessed data, worked on: as per the proposed preprocessing framework is

input to the prediction model. The output statistics from the proposed model is

discussed in section 7.

Fig. 4. Overview of the Prediction Model with Preprocessing Implemented.

7 Performance Analysis

The parameters used to evaluate the performance of the prediction model are the

accuracy, sensitivity and specificity, Receiver Operating Characteristic (ROC) and

Area under ROC (AUR). Accuracy measures the proportion of true results in a test.

Sensitivity or true positive rate, measures the proportion of actual positives which are

correctly identified while specificity, the true negative rate, measures the proportion

of negatives which are correctly identified. Receiver Operating Characteristic (ROC)

curves have been used to compare the performances of different predictive models

based on the input data set. Comparison in terms of the AUR, gives an insight to the

performance of the classifier over an entire range of values and is independent of the

GA + error

correction

used to

impute values

Identify the data type of the attribute to be

imputed

Data source

(PIDD/MMD)

Neural Network

Prediction

Model

Prediction

model output

on the imputed data

Categorical Continuous

Values Values

Use SOFM

to impute

values Complete

Data set

prevalence of the condition unlike the accuracy, which weights sensitivity and

specificity in proportion to their prevalence.

The PIDD set, when used without any preprocessing, as input to a neural networks

prediction model exhibits an accuracy of 75.82%.

When PIDD is input to the prediction model, suggested in this work, the

preprocessing framework works on the imputation of the ‘2-hour serum ‘ attribute.

The now-complete PIDD was partitioned for training and testing the model using a

random split of 80-20 split where 80% (615 records) constitute the training set and

20% (153 records) that form the test set. This splitting of data facilitates easy

validation of the model in order to improve its reliability. The performance for 4 runs

are as indicated in Table 3. The accuracy, sensitivity (the true positive rate) and

specificity (the true negative rate) calculated from the confusion matrix reflect the

performance of the prediction model.

The accuracy of the proposed model is also compared with the work presented by

Kayaer K., Yildirim T. [12] where the classifier selected was the general regression

neural network. Aslam. M. W., Nandi. A. K [13] have adopted genetic programming

with comparative partner selection (GP CPS). The results are detailed out in table 4.

Table 3. PIMA prediction accuracy details.

Imputation Accuracy Sensitivity Specificity

1 No 75.8171% 81.73% 63.27%

2 Yes 82.3529% 95.79% 73%

3 Yes 81.6993% 86% 73.59%

4 Yes 84.3137% 86.54% 79.59%

5 Yes 83.0065% 85.45% 76.74%

Table 4. Comparison of Proposed Model with works [12, 13] on PIDD data set.

Classifier Accuracy

Source data ANN 75.82

Kayaer K., Yildirim T. [12] General Regression Neural Networks 82.29

Aslam. M. W., Nandi. A. K

[13] GP 80.7

Data after Imputation ANN 82.84

MMD data, when used without any preprocessing, exhibited a prediction accuracy

of 81.3%. In this work, MMD data was prepossessed as per our proposed framework

for categorical data and data was imputed for the attribute ‘density’.

The now-complete MMD data was partitioned randomly to give a training and test

data set as per the 80-20 split. The prediction model’s accuracy is found to be

90.76%.

Accuracy, sensitivity and specificity of the model with preprocessed data are

compared with the results in [10], detailed out in table 5.

Table 5. Comparison of Proposed Model with work [10] on MMD data set.

Accuracy Sensitivity Specificity

Source data 81.34715 87.2093 76.63551

Elsayad [10] 80.9 85.29 76.97

Data after

Imputation 90.76358 87.87879 93.61702

Table 6. Area under the ROC for the prediction models.

Data Model Area Under ROC

Before Imputation After Imputation

PIDD ANN 0.7249 0.833

MMD MLP 0.8192 0.907

The ROCs for the prediction model are illustrated in Fig. 5. Imputed PIDD set is

indicated as I-PIMA and S-PIMA refers to the source/original PIMA data set, I-MMD

represents the imputed MMD as data set and S-MMD the source/original MMD data

set. The area under the ROC (AUR) is given in Table 6. An AUR of 1 represents a

perfect test; an area of 0.5 represents a worthless test. It can be observed that Neural

Network Prediction Model gives the best response on applying the soft computing

imputation method.

Fig. 5. ROC curves of prediction before and after imputation.

8 CONCLUSIONS

This work proposes a new approach for preprocessing real-time data, used for

predictive analyses and data mining in the health sector using two data sets of

diabetes and cancer prediction. The preprocessing involves two unique and distinct

processes, based on the data set considered for imputation. In case of continuous

values, CART along with optimization of the error function using a Genetic

Algorithm, maps the imputed values in a valid domain of values for each attribute.

The proposed model when tested with Pima Indian Diabetes Data set (PIDD) shows

an average accuracy of 82.84%, as against the accuracy of the prediction model when

built on data without preprocessing which is 75.816%. This improved prediction

accuracy fares superior to some of the other models in vogue. In case of categorical

values, Self Organizing Feature Maps is used to impute the selected attribute’s

incorrect values. The thus completed data is used to build the multi-layer-perceptron

model for prediction. The accuracy of the proposed model, when tested with the Mass

Mammographic Data (MMD) increased from 81.35% to 90.76%.

This framework has to be tested on similar health sector data, which tracks

ailments and the factors that influence the occurrence or recurrence of a particular

disease. The prepossessing steps that have been discussed here can be used on any

prediction model development framework, to impute missing, incorrect and

incomplete values. This approach being simpler to implement and results that are easy

to comprehend provides a reliable solution to real-time predictive analyses or other

problems that require imputation that could further contribute to the improvement of

performance of the model.

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