Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Cancer classification using Rotation Forest
Computers in Biology and Medicine
Review: Application of artificial neural networks in the diagnosis of urological dysfunctions
Expert Systems with Applications: An International Journal
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling
IEEE Transactions on Information Technology in Biomedicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease.