Accuracy for Diagnosis of Heart Disease Using Sim Supervisor Algorithm

dc.contributor.authorAbdlkareem, M.G.
dc.contributor.authorAlkhafaji, M.A.
dc.contributor.authorGüneser, M.T.
dc.date.accessioned2024-09-29T16:21:04Z
dc.date.available2024-09-29T16:21:04Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description1st National University of Science and Technology International Conference for Pure and Applied Sciences, NUSTPAS 2022 -- 1 June 2022 through 2 June 2022 -- Hybrid, Dhi-Qar -- 192840en_US
dc.description.abstractIn this paper, a model for heart disease diagnosis using semi-supervised learning methods has been proposed. The proposed method, uses Semi-Supervised Support Vector Machine (S3VM) for heart disease diagnosis. Also, Expectation Maximization (EM) method is used for handling missing values in patient's observed data; which can improve diagnosis accuracy greatly. Thus, our proposed method for diagnosing heart disease, consists of three steps: "preprocessing by EM", "extracting features by PCA" and "disease detection using S3VM". In the evaluation of the proposed method, a real dataset containing medical information of 270 patients was used. Each data record had 13 attributes, which were reduced to 10 using the PCA algorithm. The results of the proposed model has been compared with other classification algorithms. Experimental results shows that using the proposed system, we could diagnose heart disease with an average accuracy of 83%. The results of this study show that the proposed method has better performance in terms of accuracy as well as in terms of specificity and sensitivity criteria and can be a useful tool for early heart disease diagnosis. © 2023 American Institute of Physics Inc.. All rights reserved.en_US
dc.identifier.doi10.1063/5.0158016
dc.identifier.issn0094-243X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85176756725en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1063/5.0158016
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9526
dc.identifier.volume2845en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAmerican Institute of Physics Inc.en_US
dc.relation.ispartofAIP Conference Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer Aided Diagnosis (CAD)en_US
dc.subjectData Miningen_US
dc.subjectHeart Disease Diagnosisen_US
dc.subjectSemi-Supervised Learningen_US
dc.titleAccuracy for Diagnosis of Heart Disease Using Sim Supervisor Algorithmen_US
dc.typeConference Objecten_US

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