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Two Majority Voting Classifiers Applied to Heart Disease Prediction

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dc.contributor.author Karadeniz, Talha
dc.contributor.author Maraş, Hadi Hakan
dc.contributor.author Tokdemir, Gül
dc.contributor.author Ergezer, Halit
dc.date.accessioned 2024-01-25T12:34:01Z
dc.date.available 2024-01-25T12:34:01Z
dc.date.issued 2023-03
dc.identifier.citation Karadeniz, Talha;...et.al. (2023). "Two Majority Voting Classifiers Applied to Heart Disease Prediction", Applied Sciences, Vol.13, No.6. tr_TR
dc.identifier.issn 20763417
dc.identifier.uri http://hdl.handle.net/20.500.12416/6986
dc.description.abstract Two novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell–Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov–Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods’ generalization ability and success. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.3390/app13063767 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Bagging Classifier tr_TR
dc.subject Ensemble Methods tr_TR
dc.subject Gaussian Distribution tr_TR
dc.subject Heart Disease Prediction tr_TR
dc.subject Kurtosis tr_TR
dc.subject Majority Voting Classifier tr_TR
dc.title Two Majority Voting Classifiers Applied to Heart Disease Prediction tr_TR
dc.type article tr_TR
dc.relation.journal Applied Sciences (Switzerland) tr_TR
dc.contributor.authorID 34410 tr_TR
dc.contributor.authorID 293396 tr_TR
dc.identifier.volume 13 tr_TR
dc.identifier.issue 6 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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