Application of knowledge discovery process on the prediction of stroke

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridTurtay, Muhammet Gokhan/0000-0002-1728-8237
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidTurtay, Muhammet Gokhan/ABG-7401-2020
dc.contributor.authorColak, Cemil
dc.contributor.authorKaraman, Esra
dc.contributor.authorTurtay, M. Gokhan
dc.date.accessioned2024-08-04T20:40:09Z
dc.date.available2024-08-04T20:40:09Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: Stroke is a prominent life-threatening disease in the world. The current study was performed to predict the outcome of stroke using knowledge discovery process (KDP) methods, artificial neural networks (ANN) and support vector machine (SVM) models. Materials and methods: The records of 297 (130 sick and 167 healthy) individuals were acquired from the databases of the department of emergency medicine. Nine predictors (coronary artery disease, diabetes mellitus, hypertension, history of cerebrovascular disease, atrial fibrillation, smoking, the findings of carotid Doppler ultrasonography [normal, plaque, plaque + stenosis >= 50%], the levels of cholesterol and C-reactive protein) were used for predicting the stroke. Feature selection based on the Cramer's V test was carried out for reducing the predictors. Multilayer perceptron (MLP) ANN and SVM with radial basis function (RBF) kernel were used for the prediction based on the selected predictors. Results: The accuracy values were 81.82% for ANN and 80.38% for SVM in the training dataset (n = 209), and 85.9% for ANN and 84.62% for SVM in the testing dataset (n = 78), respectively. ANN and SVM models yielded area under curve (AUC) values of 0.905 and 0.899 in the training dataset, and 0.928 and 0.91 in the testing dataset, consecutively. Conclusion: The findings of the current study pointed out that ANN had more predictive performance when compared with SVM in predicting stroke. The proposed ANN model would be useful when making clinical decisions regarding stroke. (C) 2015 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2015.03.002
dc.identifier.endpage185en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.issue3en_US
dc.identifier.pmid25827533en_US
dc.identifier.scopus2-s2.0-84927178705en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage181en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2015.03.002
dc.identifier.urihttps://hdl.handle.net/11616/96745
dc.identifier.volume119en_US
dc.identifier.wosWOS:000352693600004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networks (ANN)en_US
dc.subjectKnowledge discovery process (KDP)en_US
dc.subjectSupport vector machine (SVM)en_US
dc.subjectStrokeen_US
dc.titleApplication of knowledge discovery process on the prediction of strokeen_US
dc.typeArticleen_US

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