Improving Heart Disease Diagnosis: An Ensemble Machine Learning Approach

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dc.contributor.authorNamli, Ozge H.
dc.contributor.authorYanik, Seda
dc.date.accessioned2025-02-20T08:42:24Z
dc.date.available2025-02-20T08:42:24Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 16-18, 2024 -- Istanbul Tech Univ, Canakkale, TURKEYen_US
dc.description.abstractImproving the performance of machine learning approaches in the field of health is of the utmost importance because early and correct diagnosis and treatment of diseases are essential for human life. From this point of view, an ensemble machine learning approach has been proposed for the diagnosis of heart disease within the scope of this study. In the first step of the proposed approach, feature extraction is performed using the Convolutional Neural Network on the dataset. In the next step, prediction results are obtained using individual classification methods such as Multi-layer Perceptron, Support Vector Machine, and Random Forest. Finally, the obtained prediction results are combined using the majority voting method. The results which are compared according to the critical classification performance criteria show that the proposed ensemble method gives better results than the individual methods. Heart disease can be predicted with an accuracy of 86.4% with the proposed ensemble approach.
dc.description.sponsorshipCanakkale Onsekiz Mart Univ
dc.identifier.doi10.1007/978-3-031-67192-0_12
dc.identifier.endpage100en_US
dc.identifier.isbn978-3-031-67191-3
dc.identifier.isbn978-3-031-67192-0
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85203155053
dc.identifier.scopusqualityQ4
dc.identifier.startpage92en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-67192-0_12
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1666
dc.identifier.volume1090en_US
dc.identifier.wosWOS:001329233600012
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofIntelligent and Fuzzy Systems, Vol 3, Infus 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250220
dc.subjectEnsemble Machine Learningen_US
dc.subjectClassificationen_US
dc.subjectHeart Disease Diagnosisen_US
dc.titleImproving Heart Disease Diagnosis: An Ensemble Machine Learning Approach
dc.typeConference Object

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