An expert system for the prediction of stroke disease by different least squares support vector machines models

Basit Öğe Kaydı
dc.authorscopusid55218200500
dc.authorscopusid15834365300
dc.contributor.authorSarihan M.E.
dc.contributor.authorHanbay D.
dc.date.accessioned2024-08-04T19:59:38Z
dc.date.available2024-08-04T19:59:38Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: One of the important life-threatening ailment is stroke across the world. The current paper was performed to classify the outcome of stroke by using Least-Squares Support Vector Machines (LSSVMs) models. Materials and methods: The medical dataset related to stroke disease was achieved from the clinical database of the emergency medicine department. 28 predictors were recorded in raw dataset. For dimension reduction, correlations between input and target (stroke) variables were evaluated. Different LS-SVMs models were performed with radial basis function (RBF), linear and polynomial kernels. 5- fold cross-validation was used in composing stages to achieve the best model using all of the data. The accuracy and the Area under Receiver Operating Curve (AUC ROC) values were used for performance assessment. Results: At first, feature selection stage was performed. 14 input variables were determined after this stage. Whole dataset was partitioned into 5 sub-datasets (D1,D2, D3, D4, D5) to use all data both training and testing. LS-SVMs models performance were evaluated by using 5-fold cross validation method. Accuracy and AUC values of the models were used as performance criteria. The best model performance was evaluated with LS-SVMs model using linear kernel. That model average accuracy was 86.6%. The best accuracy was evaluated with LS-SVM model using linear kernel on dataset D5 was 94%. As a consequence, the LS-SVMs model can be used for predicting the outcome of stroke. Conclusion: The results point out that LS-SVMs with linear kernel have much more accuracy and AUC values for predicting stroke disease. The suggested LS-SVMs with linear kernel may produce beneficial prediction results related to stroke disease. In future studies, several data mining techniques may be tested and assembled for better classification performance of stroke disease. © 2017, Scientific Publishers of India, All rights reserved.en_US
dc.identifier.endpage8669en_US
dc.identifier.issn0970-938X
dc.identifier.issue20en_US
dc.identifier.scopus2-s2.0-85039701814en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage8665en_US
dc.identifier.urihttps://hdl.handle.net/11616/90776
dc.identifier.volume28en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherScientific Publishers of Indiaen_US
dc.relation.ispartofBiomedical Research (India)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData miningen_US
dc.subjectLeast square support vector machines (LS-SVMs)en_US
dc.subjectStroke diseaseen_US
dc.titleAn expert system for the prediction of stroke disease by different least squares support vector machines modelsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu