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Overview of Artificial Neural Network in Medical Diagnosis

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In-Brief

A massive volume of clinical data is

produced daily that possess minute and

critical information as well as varied, in-

depth concepts of biochemistry and the

results of imaging devices. Information

provided byeach kind of data must be

evaluated and assigned for diagnostic

processes. To simplify the diagnostic

process and evade errors in that process,

artificial intelligence techniques can be

adopted like computer-aided diagnosis

and artificial neural networks. The

biostatistical services machine learning

algorithms can deal with a broad set of

specific data and produce categorized

outputs by checking the blogs in Pubrica.

Keywords: Biostatistics Services, clinical

biostatistics services, biostatistics

consulting services, biostatistics CRO,

Statistical Programming Services,

Biostatistical Services, biostatistics

consulting firms, Biostatistics for clinical

research, statistics in clinical trials,

biostatistics in clinical trials, Biostatistics

CRO, Biostatistics Support Service,

Clinical Biostatistics Services

I. INTRODUCTION

The artificial neural network has

been widely used in the fields of science

and technology. It is used for the

optimization of data. It predicts the outputs

using the input data in fields like chemical

engineering, biotechnology, healthcare,

agriculture, etc., which all handles varied

sets of data. The artificial neural network

can be used for modelling non-linear

systems with a complex system of

variables. Thus, most of the chemical

engineering and biological processes are

modelled using Artificial neural network

with the help of biostatistical consulting

services.

II. ARTIFICIAL NEURAL NETWORK

Clinical biostatistics services state

that Artificial neural network is the

simulation of human neural architecture.

The learning and generalization potentials

of human neural network inspired for the

development of an artificial neural

network. It works by taking the 70% of

input data to build a network then takes the

remaining 15% data to train itself and at

last utilize the remaining 15% data to test

itself and eventually produce the optimized

outputs.

III. ARCHITECTURE

The artificial neural network is

made up of three layers, viz., – (i) input

layer, (ii) hidden layer, (iii) output layer.

The schema of the neurons built inside the

network is based upon the complexity of

the system. The input layer collects the

input data and transfers to the hidden layer

where the data is processed to produce

optimized results with statistical

programming services. Every Artificial

neural network has an activation function

that is used for determining the output.

Each neuronisinterconnected, and each

connection has a weight attached

possessing either positive or negative

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value which tends to change upon the

training the network.

IV. OVERVIEW OF ARTIFICIAL

NEURAL NETWORK IN MEDICAL

DIAGNOSIS

Seeking various uses in various

fields of science, medical diagnosis field

also has found the application of artificial

neural network using biostatistics in

clinical services. It is used in the diagnosis

of cancer, sclerosis, diabetes, heart

diseases, etc. An adaptive algorithm is

developed and applied to yield maximum

accuracy in outputs with the statistics in

clinical trials.

V. CARDIOVASCULAR DISEASES

It is the collection of diseases

affecting the heart, cardiac muscles, blood

vessels, veins. National centre of health

statistics reported that leading cause of

death in united states of America is these

cardiovascular diseases. In the past, the

data collected from the patients were used

to develop an Artificial neural network

model with the backpropagation algorithm

was developed. This model was able to

achieve 91.2% accuracy in the diagnosis of

these diseases from the data collected.

There were other models with less than

90% accuracy also used to

diagnosespecific types of heart diseases.

VI. CANCER

In 2012, reports of American

cancer society said that more than 1.6

million newly diagnosed cases were found.

Hence, there was the need to develop a

rapid and appropriate diagnosis for clinical

management. The pertinent information

for diagnosis was collected from the

advanced analytical methods like mass

spectrometry and applied in the clinical

diagnosis of breast and ovarian cancer.

Artificial neural network is also used to

develop in diagnosing the different types

of brain tumours, lung carcinoma.

Ultimately, Artificial neural network was

seen using the ground-level data that

ranges from clinical data to results of

biochemical assays and providing

maximum diagnostic accuracy for different

types of cancer.

VII. DIABETES

Diabetes has become a severe

health risk issue in both developed and

developing countries that reaching an

estimate of 366 million diabetes cases

globally. Type ii diabetes is the standard

type of this disease which is due to the

improper cellular response to insulin

which leads to hyperglycemia. The

information of parameters like age, gender,

weight and glucose level were collected

and used as input data for building an

Artificial neural network which could able

to produce results with 90% accuracy.

Artificial neural networks are used to track

the level of glucose as well as diagnosing

diabetes according to biostatistical

research for clinical trials.

VIII. CONCLUSION

The artificial neural network can be

inferred as a powerful tool in clinical

management of diseases with several

advantages like the capability of

processing a vast set of data, reducing the

processing time, ability to produce

optimized results with maximum accuracy.

Nevertheless, Artificial neural network can

be used only as tool aiding in diagnosis

done by the clinical physician, says

biostatistical CRO, who is responsible for

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critical evaluation of the results. Pubrica

helped to understand the role of ANN tool

in the medical field.

REFERENCES

1. Al-Shayea, Q. K. (2011). Artificial neural

networks in medical diagnosis. International

Journal of Computer Science Issues, 8(2), 150-

154.

2. Amato, F., López, A., Peña-Méndez, E. M.,

Vaňhara, P., Hampl, A., & Havel, J. (2013).

Artificial neural networks in medical diagnosis.

3. Baxt, W. G. (1991). Use of an artificial neural

network for the diagnosis of myocardial

infarction. Annals of internal medicine, 115(11),

843-848.