Comparative Effectiveness of Classification Algorithms in Predicting Diabetes

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dc.contributor.authorDael, Fares A.
dc.contributor.authorMareyev, D.
dc.contributor.authorShayea, Ibraheem
dc.contributor.authorKulniyazova Korlan, S.
dc.contributor.authorAbitova, Gulnara
dc.date.accessioned2025-03-20T09:44:58Z
dc.date.available2025-03-20T09:44:58Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.descriptionIEEE MP Section; Institution of Electronics and Telecommunications Engineers (IETE)
dc.description16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
dc.description.abstractDiabetes mellitus poses a significant global health challenge, with increasing prevalence, particularly in low socioeconomic regions. Accurate and early diagnosis is crucial to prevent the severe long-term complications associated with diabetes. This study conducts a comprehensive comparison of six prominent machine learning algorithms-K-Nearest Neighbors (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forest, and Logistic Regression-in predicting diabetes using a dataset of 768 individuals with diverse diabetic indicators from Kaggle. Each algorithm is rigorously evaluated based on precision, recall, and F1-score to determine the most effective method for diabetes diagnosis. The results indicate that Logistic Regression outperforms the other algorithms, achieving an accuracy of 81%. This superior performance is attributed to Logistic Regression's ability to effectively delineate linear separations, which is crucial for distinguishing between diabetic and non-diabetic individuals. The study underscores the importance of feature selection and model tuning in enhancing predictive performance. The findings suggest that integrating Logistic Regression into clinical settings can significantly improve the accuracy and timeliness of diabetes diagnosis, potentially leading to better patient outcomes and reduced healthcare costs. © 2024 IEEE.
dc.identifier.doi10.1109/CICN63059.2024.10847398
dc.identifier.endpage1378
dc.identifier.isbn979-833150526-4
dc.identifier.scopus2-s2.0-85218074889
dc.identifier.scopusqualityN/A
dc.identifier.startpage1371
dc.identifier.urihttps://doi.org/10.1109/CICN63059.2024.10847398
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2097
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2024 IEEE 16th International Conference on Communication Systems and Network Technologies, CICN 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectDecision Trees
dc.subjectDiabetes Diagnosis
dc.subjectK-Nearest Neighbors
dc.subjectLogistic Regression
dc.subjectMachine Learning
dc.subjectNaive Bayes
dc.subjectRandom Forest
dc.subjectSupport Vector Machine
dc.titleComparative Effectiveness of Classification Algorithms in Predicting Diabetes
dc.typeConference Object

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