Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease

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dc.contributor.authorKurt, Imran
dc.contributor.authorTure, Mevlut
dc.contributor.authorKurum, A. Turhan
dc.date.accessioned2024-06-12T11:07:47Z
dc.date.available2024-06-12T11:07:47Z
dc.date.issued2008
dc.departmentTrakya Üniversitesien_US
dc.description.abstractIn this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAT) and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing feature maps (SOFM). Predictor variables were age, sex, family history of CAD, smoking status, diabetes mellitus, systemic hypertension, hypercholesterolemia, and body mass index (BMI). Performances of classification techniques were compared using ROC curve, Hierarchical Cluster Analysis (HCA), and Multidimensional Scaling (MDS). Areas under the ROC curves are 0.783, 0.753, 0.745, 0.721, and 0.675, respectively for MLP, LR, CART, RBF, and SOFM. MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance. MLP, CART, LR, and RBF performed better than SOFM in predicting CAD in according to HCA and MDS. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2006.09.004
dc.identifier.endpage374en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-34248647301en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage366en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2006.09.004
dc.identifier.urihttps://hdl.handle.net/20.500.14551/22187
dc.identifier.volume34en_US
dc.identifier.wosWOS:000250295300036en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
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/closedAccessen_US
dc.subjectLogistic Regressionen_US
dc.subjectDecision Treeen_US
dc.subjectNeural Networksen_US
dc.subjectCoronary Artery Diseaseen_US
dc.subjectMultidimensional Scalingen_US
dc.subjectHierarchical Cluster Analysisen_US
dc.subjectROC Curveen_US
dc.subjectRisk-Factorsen_US
dc.subjectAtherosclerosisen_US
dc.subjectCurvesen_US
dc.subjectMenen_US
dc.titleComparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery diseaseen_US
dc.typeArticleen_US

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