Application of data mining techniques for detecting asymptomatic carotid artery stenosis

Authors:
Ugur Bilge;Selen Bozkurt;Sedat Durmaz
Affiliations:
Akdeniz University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Antalya, Turkey;Akdeniz University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Antalya, Turkey;Akdeniz University, Faculty of Medicine, Department of Radiology, Antalya, Turkey
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

Asymptomatic carotid stenosis, one of the etiological factors for stroke, has several risk factors such as hypertension, cardiac morbidity, smoking, diabetes, and physical inactivity. Understanding and determining factors that predispose to asymptomatic carotid stenosis will help in the design of acute stroke trials and in prevention programs. The goal of this study is to explore rules and relationships that might be used to detect possible asymptomatic carotid stenosis by using data mining techniques. For this purpose, Genetic Algorithms (GAs), Logistic Regression (LR), and Chi-square tests have been applied to the patient dataset. Results of these tests have also been compared.