Comparison of machine learning methods for a diabetes prediction information system

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dc.contributor.authorShmatko, O. V.en
dc.contributor.authorKorol, O.en
dc.contributor.authorTkachov, A.en
dc.contributor.authorOtenko, V.en
dc.contributor.authorШматко, О. В.uk
dc.date.accessioned2023-05-06T21:11:57Z
dc.date.available2023-05-06T21:11:57Z
dc.date.issued2021
dc.description.abstractDiabetes is a disease for which there is no permanent cure; therefore, methods and information systems are required for its early detection. This paper proposes an information system for predicting diabetes based on the use of data mining methods and machine learning (ML) algorithms. The paper discusses a number of machine learning methods such as decision trees (DT), logistic regression (LR), k-Nearest Neighbors (k-NN). For our research, we used the Pima Indian Diabetes (PID) dataset collected from the UCI machine learning repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. Research has been carried out to improve the prediction index based on the Recursive Feature Elimination method. We found that the logistic regression (LR) model performed well in predicting diabetes. We have shown that in order to use the created model topredict 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 oralglucose tolerance test, the BMI index and the result of the calculation. heredity functions "DiabetesPedigreeFunction"en
dc.identifier.citationShmatko, O., Korol, O., Tkachov, A., & Otenko, V. (2021). Comparison of machine learning methods for a diabetes prediction information system. Intellectual Systems and Information Technologies (ISIT 2021) : short Paper Proceedings of the 2nd International Conference. CEUR Workshop Proceedings, 3126, 192–197.en
dc.identifier.citationShmatko O., Korol O., Tkachov A., Otenko V. Comparison of machine learning methods for a diabetes prediction information system. Intellectual Systems and Information Technologies (ISIT 2021) : short Paper Proceedings of the 2nd International Conference. CEUR Workshop Proceedings. 2021. Vol. 3126. P. 192–197.en
dc.identifier.issn1613-0073
dc.identifier.orcidhttps://orcid.org/0000-0002-2426-900X
dc.identifier.orcidhttps://orcid.org/0000-0002-8733-9984
dc.identifier.orcidhttps://orcid.org/0000-0003-1428-0173
dc.identifier.orcidhttps://orcid.org/0000-0002-5979-1084
dc.identifier.urihttps://dspace.mipolytech.education/handle/mip/218
dc.language.isoenen
dc.publisherCEUR Workshop Proceedingsen
dc.relation.ispartofIntellectual Systems and Information Technologies (ISIT 2021) : short Paper Proceedings of the 2nd International Conference. CEUR Workshop Proceedings. Vol. 3126 : 192–197.en
dc.subjectmachine learningen
dc.subjectdata miningen
dc.subjectneural networken
dc.subjectdiabetes prediction information systemen
dc.subjectknnen
dc.subjectlogistic regressionen
dc.subjectdecision treeen
dc.titleComparison of machine learning methods for a diabetes prediction information systemen
dc.typeArticle
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