Comparing classification techniques for predicting essential hypertension

Basit Öğe Kaydı
dc.contributor.authorTure, M
dc.contributor.authorKurt, I
dc.contributor.authorKurum, AT
dc.contributor.authorOzdamar, K
dc.date.accessioned2024-06-12T11:07:48Z
dc.date.available2024-06-12T11:07:48Z
dc.date.issued2005
dc.departmentTrakya Üniversitesien_US
dc.description.abstractHypertension is a leading cause of heart disease and stroke. In this study, performance of classification techniques is compared in order to predict the risk of essential hypertension disease. A retrospective analysis was performed in 694 subjects (452 patients and 242 controls). We compared performances of three decision trees, four statistical algorithms, and two neural networks. Predictor variables were age, sex, family history of hypertension, smoking habits, lipoprotein (a), triglyceride, uric acid, total cholesterol, and body mass index (BMI). Classification techniques were grouped using hierarchical cluster analysis (HCA). The data points appeared to cluster in three groups. The first cluster included MLP and RBF. Furthermore CART which was more similar than other techniques linked this cluster. The second cluster included FDA/MARS (degree= 1), LR and QUEST, but FDA/MARS (degree= 1) and LR was more similar than QUEST. The third cluster included FDA/MARS (degree =2), CHAID and FDA, but FDA/MARS (degree =2) and CHAID was more similar than FDA. MLP and RBF which are one each of neural networks procedures, performed better than other techniques in predicting hypertension. QUEST had a lesser performance than other techniques. (c) 2005 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2005.04.014
dc.identifier.endpage588en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-24144490004en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage583en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2005.04.014
dc.identifier.urihttps://hdl.handle.net/20.500.14551/22188
dc.identifier.volume29en_US
dc.identifier.wosWOS:000231659400009en_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.subjectFDAen_US
dc.subjectMARSen_US
dc.subjectDecision Treeen_US
dc.subjectNeural Networksen_US
dc.subjectHierarchical Cluster Analysisen_US
dc.subjectHypertensionen_US
dc.subjectBlood-Pressureen_US
dc.subjectRisken_US
dc.titleComparing classification techniques for predicting essential hypertensionen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu