Application of knowledge discovery process on the prediction of stroke. Issue 3 (May 2015)

• Record Type: Journal Article
• Authors: Colak, Cemil; Karaman, Esra; Turtay, M. Gokhan
• Is Part Of: Computer methods and programs in biomedicine. Volume 119:Issue 3(2015)
• Page Start: 181
• Page End: 185
• Publication Date: 2015-05
• Subjects: Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.03.002
• Languages: English
• ISSNs: 0169-2607

Abstract

<abstract abstract-type="author-highlights" id="abs0005"> <title id="sect0005">Highlights</title> <sec> <p id="spar0005"> <list id="lis0005"> <list-item id="lsti0005"> <label></label> <p id="par0005">We predicted the outcome of stroke using knowledge discovery process (KDP) methods, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models.</p> </list-item> <list-item id="lsti0010"> <label></label> <p id="par0010">The performance of the models, assessed as accuracy and area under curve (AUC), for predicting stroke was 85.9% and 0.928 for ANN, and 84.62% and 0.91 for SVM, respectively.</p> </list-item> <list-item id="lsti0015"> <label></label> <p id="par0015">KDP was used for providing better understanding of the stroke data, and reducing unnecessary processes to be performed.</p> </list-item> <list-item id="lsti0020"> <label></label> <p id="par0020">The proposed ANN model predicted successfully the stroke disease based on the selected predictors.</p> </list-item> </list> </p> </sec> </abstract>