Open AccessCOMPARISON OF MACHINE LEARNING METHODS FOR A DIABETES PREDICTION INFORMATION SYSTEM
Author(s) -
Olexander Shmatko,
Anna Goloskokova,
Olha Korol,
Irada Rahimova
Publication year - 2021
Publication title -
sistemi upravlìnnâ, navìgacìï ta zvʼâzku
Language(s) - English
Resource type - Journals
ISSN - 2073-7394
DOI - 10.26906/sunz.2021.4.073
Subject(s) - artificial intelligence , logistic regression , machine learning , computer science , multilayer perceptron , diabetes mellitus , support vector machine , adaboost , random forest , artificial neural network , body mass index , regression , medicine , mathematics , statistics , endocrinology
Diabetesisadiseaseforwhichthereisnopermanentcure;therefore,methodsandinformationsystemsarerequired for its early detection. This paper proposes an information system for predicting diabetes based on the use of data mining methods and machine learning algorithms. The paper discusses a number of machine learning methods such as random forest, AdaBoost algorithm, multilayer perceptron, neuro–like structure of Consecutive Geometric Transformations Models (CGTM), linear regression based on the stochastic gradient descent, generalized regression neural network and regression based on the support vector machine. The Pima Indian Diabetes dataset collected from the UCI machine learning repository was used in the research. The dataset contains information about 768 patients and their corresponding nine unique attributes: the number of times of pregnancy; plasma glucose concentration for two hours in an oral glucose tolerance test; diastolic blood pressure; the thickness of the folds of the skin of the triceps; the concentration of serum insulin for two hours; body mass index; a function of diabetes heredity; the age of a person; the result of a variable class (0 – no diabetes, 1 – a sick person). The research has been carried out to improve the prediction index based on the Recursive Feature Elimination method. It was found that the logistic regression model performed well in predicting diabetes. It has been shown that in order to use the created model to predict the likelihood of diabetes mellitus with an accuracy of 78%, it is necessary and sufficient to use such indicators of the patient's health status as the number of times of pregnancy, the concentration of glucose in the blood plasma during the oral glucose tolerance test, the BMI index and the result of the calculation of the heredity functions "Diabetes Pedigree Function".