Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Table 3

Review of state-of-the-art classifiers and their effectiveness.

Work	Feature set	Classifiers	Effectiveness
[8]	A	Optimized SVM	Accuracy = 99.2%; Sensitivity = 98.43%; Specificity = 100%
[66]	B	NN	Accuracy = 88.4%
[10]	A	KNN	Accuracy = 96.8%; Sensitivity = 100%; Specificity = 93.7%
[9]	A	LS-SVM	Accuracy = 99.7%; Sensitivity = 99.6%; Specificity = 99.8%
[27]	A	SVM	Accuracy = 79.71%
[7]	A	LS-SVM	Accuracy = 100%
[19]	B	Fuzzy rule	Accuracy = 84%; Sensitivity = 79%; Specificity = 89%
[32]	B	Fuzzy rule	Accuracy = 92.8%
[58]	B	Fuzzy rule	Accuracy = 81.2%
[67]	B	Fuzzy rule and ensemble classifier	Accuracy = 84.44%
[55]	A	Random forest	Sensitivity = 80%; Specificity = 90%
[44]	A	SVM with RBF	Sensitivity = 73%; Specificity = 87%
[45]	A	SVM	Sensitivity = 85%; Specificity = 78%