Abstrait

Automated Diagnosis of Coronary Artery Disease: A Review and Work flow

John parker

Abstract
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.