Stochastic search system for predicting diabetes based on the genetic method

I. Fedorchenko; A. Oliinyk; J.A.J. Alsayaydeh; A Stepanenko; T. Fedoronchak; T. Kolpakova; A. Kharchenko

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Stochastic search system for predicting diabetes based on the genetic method

Author(s)

I. Fedorchenko Department of Software Tools, National University «Zaporizhzhia Polytechnic»,Zhukovskoho str., Zaporizhzhya,Ukraine

A. Oliinyk Department of Software Tools, National University «Zaporizhzhia Polytechnic»,Zhukovskoho str., Zaporizhzhya,Ukraine

J.A.J. Alsayaydeh Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,Durian Tunggal, Melaka,Malaysia

A Stepanenko Department of Software Tools, National University «Zaporizhzhia Polytechnic»,Zhukovskoho str., Zaporizhzhya,Ukraine

T. Fedoronchak Department of Software Tools, National University «Zaporizhzhia Polytechnic»,Zhukovskoho str., Zaporizhzhya,Ukraine

T. Kolpakova Department of Software Tools, National University «Zaporizhzhia Polytechnic»,Zhukovskoho str., Zaporizhzhya,Ukraine

A. Kharchenko Department of Software Tools, National University «Zaporizhzhia Polytechnic»,Zhukovskoho str., Zaporizhzhya,Ukraine

Abstract

The paper considers the field of software engineering related to the parametric synthesis of neural networks based on an evolutionary approach and its application in the diagnosis of diabetes. As a result of the research, an intelligent decision support system for diagnosing diabetes was developed, which is based on machine learning models. The model of parametric optimization of the neural network due to the use of genetic algorithm and particle swarm method is implemented. A modified genetic method for optimizing neural network parameters to solve the problem of predicting the risk of diabetes has been developed. Modification of a simple genetic algorithm, which is implemented as part of the project allows you to speed up the selection of learning parameters of neural networks, and increase the resulting accuracy compared to the basic version of a simple genetic algorithm, by modifying the mutation operator, as well as a changed approach to the selection of individuals for crossing. The developed model is intended for use in the field of medical care and allows to determine with some accuracy the presence of the risk of diabetes in patients by clinical indicators of health. The result of this model is to reduce the likelihood of a doctor's error, increase the doctor's confidence in the decision made in the diagnosis and more lives saved, by making a correct and timely diagnosis. The developed model is intended for use in the field of medical care and allows to determine with some accuracy the presence of the risk of diabetes in patients by clinical indicators of health. The result of this model is to reduce the likelihood of a doctor's error, increase the doctor's confidence in the decision when making a diagnosis and more lives saved, by making a correct and timely diagnosis. The developed model is intended for use in the field of medical care and allows to determine with some accuracy the presence of the risk of diabetes in patients by clinical indicators of health. The result of this model is to reduce the likelihood of a doctor's error, increase the doctor's confidence in the decision when making a diagnosis and more lives saved, by making a correct and timely diagnosis.

Publication Details

Page(s) 445-461

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 17, Issue: 4, Year: 2022

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Keywords

Prediction, genetic algorithm, Keras, Diabetes, Classification, Neural Network, PSO, Diabetes, Python

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