Application of data mining techniques for detecting asymptomatic carotid artery stenosis

BİLGE U., Bozkurt S., Durmaz S.

COMPUTERS & ELECTRICAL ENGINEERING, cilt.39, sa.5, ss.1499-1505, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 5
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.compeleceng.2012.12.010
  • Dergi Adı: COMPUTERS & ELECTRICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1499-1505
  • Akdeniz Üniversitesi Adresli: Evet

Özet

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 data-set. Results of these tests have also been compared. (C) 2012 Elsevier Ltd. All rights reserved.