A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease
Yükleniyor...
Dosyalar
Tarih
2010
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer-Verlag Berlin
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The aim of this study is to show a comparison of multi-layered perceptron neural network (MLPNN) and support vector machine (SVM) on determination of coronary artery disease existence upon exercise stress testing (EST) data. EST and coronary angiography were performed on 480 patients with acquiring 23 verifying features from each. The robustness of the proposed methods is examined using classification accuracy, k-fold cross-validation method and Cohen's kappa coefficient. The obtained classification accuracies are approximately 78% and 79% for MLPNN and SVM respectively. Both MLPNN and SVM methods are rather satisfactory than human-based method looking to Cohen's kappa coefficients. Besides, SVM is slightly better than MLPNN when looking to the diagnostic accuracy, average of sensitivity and specificity, and also Cohen's kappa coefficient.
Açıklama
4th International Conference on Advances in Information Technology (IAIT) -- NOV 04-05, 2010 -- Bangkok, THAILAND
Anahtar Kelimeler
Exercise stress testing, Coronary artery disease, Support vector machine, Artificial neural networks
Kaynak
Advances in Information Technology
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
114
Sayı
Künye
Babaoğlu, İ., Baykan, Ö. K., Aygül, N., Özdemir, K., Bayrak, M., (2010). A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease. Advances in Information Technology, (114), 18-26.
