Application of Data Mining Techniques for Detecting Asymptomatic Carotid Artery Stenosis
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 (GA), Logistic Regression (LR), and Chi-square tests have been applied to the patient dataset. Results of these tests have also been compared.
genetic algorithms logistic regression data mining radiology asymptomatic carotid artery stenosis
Ugur Bilge Selen Bozkurt Sedat Durmaz Kemal Hakan Gulkesen Saim Yilmaz
Akdeniz University, Faculty of Medicine Department of Biostatistics and Medical Informatics, Antalya Akdeniz University, Faculty of Medicine Department of Radiology, Antalya, Turkey
国际会议
上海
英文
1666-1669
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）