Use of neural networks in predicting the risk of coronary artery disease
Computers and Biomedical Research
The nature of statistical learning theory
The nature of statistical learning theory
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Bio-medical entity extraction using support vector machines
Artificial Intelligence in Medicine
Neural network predictions of significant coronary artery stenosis in men
Artificial Intelligence in Medicine
Risk prediction for postoperative morbidity of endovascular aneurysm repair using ensemble model
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Journal of Medical Systems
Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis
Journal of Medical Systems
Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis
International Journal of Knowledge Discovery in Bioinformatics
Computer Methods and Programs in Biomedicine
Linear and nonlinear analysis of normal and CAD-affected heart rate signals
Computer Methods and Programs in Biomedicine
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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.