Improvement of machine learning-based diabetes diagnosis via resampling techniques

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dc.contributor.authorYapici, Irem Senyer
dc.contributor.authorArslan, Rukiye Uzun
dc.contributor.authorEngin, Mustafa Alptekin
dc.date.accessioned2026-09-01T15:52:33Z
dc.date.available2026-09-01T15:52:33Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractThe objective of this study is to enhance the accuracy of diabetes diagnosis through the utilisation of machine learning techniques and resampling methods. The imbalanced nature of diabetes datasets presents a significant challenge for traditional classification algorithms, which often struggle to accurately predict results. In order to enhance the efficacy of the model, a comparative analysis was conducted to assess the performance of a range of over-sampling and under-sampling techniques, including SMOTE, ADASYN, Borderline SMOTE, SVM SMOTE, Random Under Sampler, Near Miss, One Sided Selection, Neighbourhood Cleaning Rule, Edited Nearest Neighbours, Instance Hardness Threshold, AllKNN and Tomek Links. The aforementioned techniques were then applied to the Decision Tree, Random Forest, K-Nearest Neighbours, AdaBoost, Extra Tree Classifier, and machine learning classifiers, and their performance was evaluated using the accuracy, recall, precision, F-Score, and AUC-ROC performance metrics. The SVMSMOTE resampling technique was identified as the most successful method, achieving 99.06% accuracy when used in combination with the decision tree classifier. The findings demonstrate that the incorporation of resampling techniques markedly enhances diagnostic proficiency and yields more dependable forecasts. This research makes a significant contribution to the field of medical informatics, providing a robust framework for diabetes diagnosis and offering valuable insights into the application of machine learning healthcare.
dc.identifier.doi10.5505/pajes.2025.52882
dc.identifier.endpage258
dc.identifier.issn1300-7009
dc.identifier.issn2147-5881
dc.identifier.issue2
dc.identifier.startpage247
dc.identifier.trdizinid1394274
dc.identifier.urihttp://dx.doi.org/10.5505/pajes.2025.52882
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1394274
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8430
dc.identifier.volume32
dc.identifier.wosWOS:001785886700003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherPamukkale Univ
dc.relation.ispartofPamukkale University Journal of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260820
dc.subjectDiabetes Diagnosis
dc.subjectResampling Techniques
dc.subjectImbalanced Dataset
dc.subjectMachine Learning
dc.titleImprovement of machine learning-based diabetes diagnosis via resampling techniques
dc.typeArticle

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