Effects of Principle Component Analysis on Assessment of Coronary Artery Diseases Using Support Vector Machine

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dc.contributor.authorBabaoğlu, İsmail
dc.contributor.authorFındık, Oğuz
dc.contributor.authorBayrak, Mehmet
dc.date.accessioned2020-03-26T17:48:19Z
dc.date.available2020-03-26T17:48:19Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractArtificial intelligence techniques are being effectively used in medical diagnostic support tools to increase the diagnostic accuracy and to provide additional knowledge to medical stuff. Effects of principle component analysis on the assessment of exercise stress test with support vector machine in determination of coronary artery disease are studied in this work. Study dataset consist of 480 patients with 23 features for each patient. By reducing study dataset with principle component analysis method, optimum support vector machine model is found for each reduced dimension. According to the obtained results, optimum support vector machine model in which the dataset is reduced to 18 features with principle component analysis is more accurate than optimum support vector machine model which uses the whole 23 featured dataset. Besides, principle component analysis implementation decreases the training error and the sum of the training and test times.en_US
dc.identifier.citationBabaoğlu, İ., Fındık, O., Bayrak, M., (2010). Effects of Principle Component Analysis on Assessment of Coronary Artery Diseases Using Support Vector Machine. Expert Systems with Applications, (37), 2182-2185. Doi: 10.1016/j.eswa.2009.07.055
dc.identifier.doi10.1016/j.eswa.2009.07.055en_US
dc.identifier.endpage2185en_US
dc.identifier.issn0957-4174en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2182en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2009.07.055
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24864
dc.identifier.volume37en_US
dc.identifier.wosWOS:000272846500041en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBabaoğlu, İsmail
dc.institutionauthorFındık, Oğuz
dc.institutionauthorBayrak, Mehmet
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.subjectSupport vector machineen_US
dc.subjectPrinciple component analysisen_US
dc.subjectCoronary artery diseaseen_US
dc.subjectExercise stress testen_US
dc.titleEffects of Principle Component Analysis on Assessment of Coronary Artery Diseases Using Support Vector Machineen_US
dc.typeArticleen_US

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