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Cardiology Research and Practice
Volume 2018, Article ID 2016282, 9 pages
Review Article

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia

Correspondence should be addressed to Teh Ying Wah;

Received 27 October 2017; Accepted 19 December 2017; Published 4 February 2018

Academic Editor: Stephan von Haehling

Copyright © 2018 Qurat-ul-ain Mastoi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.