A NOVEL HYBRID CLASSIFICATION METHOD WITH PARTICLE SWARM OPTIMIZATION AND K-NEAREST NEIGHBOR ALGORITHM FOR DIAGNOSIS OF CORONARY ARTERY DISEASE USING EXERCISE STRESS TEST DATA

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dc.contributor.authorBabaoglu, Ismail
dc.contributor.authorFindik, Oguz
dc.contributor.authorUlker, Erkan
dc.contributor.authorAygul, Nazif
dc.date.accessioned2020-03-26T18:23:36Z
dc.date.available2020-03-26T18:23:36Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe aim of this study is to investigate the effectiveness of a novel hybrid method, particle swarm optimization with k-nearest neighbor classifier (PSOkNN), on determination of coronary artery disease (CAD) existence upon exercise stress testing (EST) data. The PSOkNN method is composed of two steps. At the first step, one particle which demonstrates the whole samples optimally in training dataset is generated for both healthy and unhealthy patients. Then, at the second one, the class of the test sample is determined according to the distance of the test sample to the generated particles utilizing k-nearest neighbor algorithm. To demonstrate the effectiveness of this novel method, the results of PSOkNN are compared with the classification results of the artificial immune recognition system and k-nearest neighbor algorithm. Besides, reliability of the proposed method on determination of CAD existence upon EST data is examined by using classification accuracy, k-fold cross-validation method and Cohen's kappa coefficient.en_US
dc.identifier.endpage3475en_US
dc.identifier.issn1349-4198en_US
dc.identifier.issn1349-418Xen_US
dc.identifier.issue5Ben_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3467en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27688
dc.identifier.volume8en_US
dc.identifier.wosWOS:000305169200003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherICIC INTERNATIONALen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROLen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectCoronary artery diseaseen_US
dc.subjectExercise stress testingen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectArtificial immune recognition systemen_US
dc.titleA NOVEL HYBRID CLASSIFICATION METHOD WITH PARTICLE SWARM OPTIMIZATION AND K-NEAREST NEIGHBOR ALGORITHM FOR DIAGNOSIS OF CORONARY ARTERY DISEASE USING EXERCISE STRESS TEST DATAen_US
dc.typeArticleen_US

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