A Comparison of Feature Selection Models Utilizing Binary Particle Swarm Optimization and Genetic Algorithm in Determining Coronary Artery Disease Using Support Vector Machine

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dc.contributor.authorBabaoğlu, İsmail
dc.contributor.authorFındık, Oğuz
dc.contributor.authorÜlker, Erkan
dc.date.accessioned2020-03-26T17:46:38Z
dc.date.available2020-03-26T17:46:38Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe aim of this study is to search the efficiency of binary particle swarm optimization (BPSO) and genetic algorithm (CA) techniques as feature selection models on determination of coronary artery disease (CAD) existence based upon exercise stress testing (EST) data. Also, increasing the classification performance of the classifier is another aim. The dataset having 23 features was obtained from patients who had performed EST and coronary angiography. Support vector machine (SVM) with k-fold cross-validation method is used as the classifier system of CAD existence in both BPSO and CA feature selection techniques. Classification results of feature selection technique using BPSO and CA are compared with each other and also with the results of the whole features using simple SVM model. The results show that feature selection technique using BPSO is more successful than feature selection technique using CA on determining CAD. Also with the new dataset composed by feature selection technique using BPSO, this study reached more accurate values of success on CAD existence research with more little complexity of classifier system and more little classification time compared with whole features used SVM.en_US
dc.identifier.citationBabaoğlu, İ., Fındık, O., Ülker, E., (2010). A Comparison of Feature Selection Models Utilizing Binary Particle Swarm Optimization and Genetic Algorithm in Determining Coronary Artery Disease Using Support Vector Machine. Expert Systems with Applications, 37(4), 3177-3183. Doi: 10.1016/j.eswa.2009.09.064
dc.identifier.doi10.1016/j.eswa.2009.09.064en_US
dc.identifier.endpage3183en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3177en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2009.09.064
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24506
dc.identifier.volume37en_US
dc.identifier.wosWOS:000274202900054en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBabaoğlu, İsmail
dc.institutionauthorFındık, Oğuz
dc.institutionauthorÜlker, Erkan
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBinary particle swarm optimizationen_US
dc.subjectGenetic algorithmen_US
dc.subjectSupport vector machineen_US
dc.subjectExercise stress testingen_US
dc.subjectCoronary artery diseaseen_US
dc.titleA Comparison of Feature Selection Models Utilizing Binary Particle Swarm Optimization and Genetic Algorithm in Determining Coronary Artery Disease Using Support Vector Machineen_US
dc.typeArticleen_US

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