Effects of Principle Component Analysis on Assessment of Coronary Artery Diseases Using Support Vector Machine
Yükleniyor...
Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Artificial intelligence techniques are being effectively used in medical diagnostic support tools to increase the diagnostic accuracy and to provide additional knowledge to medical stuff. Effects of principle component analysis on the assessment of exercise stress test with support vector machine in determination of coronary artery disease are studied in this work. Study dataset consist of 480 patients with 23 features for each patient. By reducing study dataset with principle component analysis method, optimum support vector machine model is found for each reduced dimension. According to the obtained results, optimum support vector machine model in which the dataset is reduced to 18 features with principle component analysis is more accurate than optimum support vector machine model which uses the whole 23 featured dataset. Besides, principle component analysis implementation decreases the training error and the sum of the training and test times.
Açıklama
Anahtar Kelimeler
Support vector machine, Principle component analysis, Coronary artery disease, Exercise stress test
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
37
Sayı
Künye
Babaoğlu, İ., Fındık, O., Bayrak, M., (2010). Effects of Principle Component Analysis on Assessment of Coronary Artery Diseases Using Support Vector Machine. Expert Systems with Applications, (37), 2182-2185. Doi: 10.1016/j.eswa.2009.07.055
