Performance Comparison of Machine Learning Models for Diabetes Prediction

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dc.contributor.authorCihan, Pınar
dc.contributor.authorCoşkun, Hakan
dc.date.accessioned2023-04-20T08:04:13Z
dc.date.available2023-04-20T08:04:13Z
dc.date.issued2021
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK
dc.description.abstractDiabetes is a chronic disease that causes blood sugar to rise. This chronic disease can be cause mortality. There are different diagnoses and treatment methods for diabetes in the medical field. In addition, with the developing technology, diagnosis of the disease can be made computer-aided. Computeraided diagnostic methods are a successful, fast, and alternative method that supports the doctor's decision. The use of computeraided diagnosis approach for diabetes and many other diseases is increasing day by day. Machine learning classification methods are the most commonly used methods for computer-aided diagnostics. The aim of this study is to design a model to detect the probability of diabetes in patients at an early stage with maximum accuracy. Therefore, seven machine learning classification algorithms were used, namely Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest, and Artificial Neural Network. The study was carried out on the Pima Indians Diabetes Database (PIDD) taken from the Kaggle database. The performances of machine learning methods were evaluated according to precision, recall, ROC curve, and PRC criteria. According to the results, the Logistic Regression method is more successful than other methods in classifying diabetes disease accurately.
dc.description.sponsorshipIEEE,IEEE Turkey Sect
dc.identifier.doi10.1109/SIU53274.2021.9477824
dc.identifier.isbn978-1-6654-3649-6
dc.identifier.scopus2-s2.0-85111427219
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9477824
dc.identifier.urihttps://hdl.handle.net/20.500.11776/11019
dc.identifier.wosWOS:000808100700067
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorCoşkun, Hakan
dc.institutionauthorCihan, Pınar
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof29th Ieee Conference On Signal Processing and Communications Applications (Siu 2021)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine Learning
dc.subjectDiabetes
dc.subjectClassification
dc.subjectLogistic Regression
dc.subjectRoc
dc.subjectPrc
dc.subjectDiagnosis
dc.titlePerformance Comparison of Machine Learning Models for Diabetes Prediction
dc.typeConference Object

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