Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection

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dc.contributor.authorTurk, Fuat
dc.date.accessioned2025-01-21T16:41:56Z
dc.date.available2025-01-21T16:41:56Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractHeart diseases are an essential research topic in healthcare institutions around the world. Therefore, using machine learning and optimization algorithms attracts attention as an important method in detecting heart diseases. Additionally, the factors that affect heart disease are a matter of current debate. In this study, an effective DFD method is proposed using optimization techniques for classifying heart diseases and examining the factors affecting the disease. Initially, the study employs classical machine learning and ensemble algorithms for classification. Subsequently, feature selection is performed using BEO, BSPO, GA, and GFO methods, and the importance levels of features are determined utilizing the DFD approach. The results indicate that the ensemble model achieved an accuracy of 86.34% without optimization methods, whereas the proposed DFD method, when applied in conjunction with ensemble models, increased the accuracy to 99.08%. Therefore, it is observed that ensemble models yield the highest results when used in conjunction with optimization algorithms. The outcomes identified using the DFD method, which are clinically significant, are believed to hold great importance in reducing the number of heart patients and enhancing treatment.
dc.description.sponsorshipFaculties of Engineering at Kimath;rimath;kkale University
dc.description.sponsorshipThe author gratefully acknowledges the partial support of the Faculties of Engineering at K & imath;r & imath;kkale University.
dc.identifier.doi10.1007/s11760-024-03060-0
dc.identifier.endpage3955
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85186552625
dc.identifier.scopusqualityQ2
dc.identifier.startpage3943
dc.identifier.urihttps://doi.org/10.1007/s11760-024-03060-0
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24985
dc.identifier.volume18
dc.identifier.wosWOS:001173465300003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectDominant feature detection; Optimization algorithms; Heart disease; Ensemble learning; Machine learning
dc.titleInvestigation of machine learning algorithms on heart disease through dominant feature detection and feature selection
dc.typeArticle

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