## Appendix A  
Table 14 listed classification performances of linear-SVM, polynomial-SVM, and RBF-SVM, by using the parameter optimization method of grid search. The results indicated that the values of the classification performances (training accuracy, testing accuracy, testing sensitivity, and testing specificity) for the specific SVM model were similar in all sampling cases. Moreover, the fluctuations of the classification performances of all the SVM models were within 5%. The fluctuation of these classification performances was even less than 1%o of the polynomial-SVM model. This suggested that the classification performances were not significantly affected by the data sampling, no matter what SVM algorithm was used. Moreover, the results showed that classification performances of accuracy, sensitivity, and specificity of all the SVM models were over 95%, and the values of polynomial-SVM were the best (almost all were 100%). This suggested that SVM models were very effective and stable to detect CAD by combining the measured morphological features, especially for polynomial-SVM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Year** & **Machine Learning Algorithms** & **Medical Features Datasets** & **Accuracy (\%)** \\ \hline \hline \multirow{6}{*}{[PERSON] et al. [12]} & \multirow{6}{*}{2020} & Decision tree (DT) & 77.53 \\ \cline{3-4}  & & Gaussian Naive Bayes (GNB) & \multirow{2}{*}{Long beach dataset selected by ADASYN} & 79.44 \\ \cline{3-4}  & & Random Forest (RF) & 80.45 \\ \cline{3-4}  & & Extreme Gradient Boosting & 80.41 \\ \cline{3-4}  & & (CGBoost) & 88.12 \\ \cline{3-4}  & & DT & 88.12 \\ \cline{3-4}  & & GNB & 88.72 \\ \cline{3-4}  & & RF & 91.56 \\ \cline{3-4}  & & XGBoost & 92.35 \\ \cline{3-4}  & & Deep learning (DL) & \multirow{2}{*}{Coronary computed tomography angiography (CCTA)} & 87.10 \\ \hline \multirow{2}{*}{[PERSON] et al. [26]} & \multirow{2}{*}{2019} & Nested ensemble nu-Support Vector Classification (NE-nu-SVC) & \multirow{2}{*}{Cleveland} & 98.60 \\ \cline{3-4}  & & Taylorgradient descent & \multirow{2}{*}{Hungarian} & \multirow{2}{*}{82.55} \\ \cline{3-4}  & & (TGD)-based actor critic neural network (ACNN) & & \\ \hline \multirow{2}{*}{[PERSON] et al. [39]} & \multirow{2}{*}{2019} & Hybrid particle swarm optimization (PSO) & Z-Alizadeh Sani & 84.2 \\ \cline{3-4}  & & & Electrocardiogram (ECG) and & \\ \cline{3-4}  & & & phonocardiogram (PCC) signals & \\ \hline \multirow{2}{*}{[PERSON] et al. [40]} & \multirow{2}{*}{2019} & \multirow{2}{*}{DL} & Myocardial perfusion imaging (MP) & 84.8 \\ \cline{3-4}  & & & (MP) & \\ \hline \multirow{2}{*}{[PERSON] et al. [25]} & \multirow{2}{*}{2019} & N2 Genetic-nuSVM & [PERSON] & 93.08 \\ \cline{3-4}  & & 2019 & NE-nu-SVC & [PERSON] and Cleveland & 94.66 and 98.60 \\ \hline \multirow{2}{*}{[PERSON] et al. [41]} & \multirow{2}{*}{2017} & Ensemble algorithm based on & \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{93.70} \\  & & (EA-MFS) & & \\ \hline \multirow{2}{*}{[PERSON] et al. [42]} & \multirow{2}{*}{2017} & Double Density-Dual Tree DWT (DD-DTDWT) & Ultrasound images & 96.05 \\ \hline \multirow{2}{*}{[PERSON] et al. [43; 44]} & \multirow{2}{*}{2016} & Support Vector Machine (SVM) & \multirow{2}{*}{[PERSON]} & 86.14 \\ \cline{3-4}  & & Sequential Minimal Optimization (SMO) & & 94.08 \\ \hline \multirow{6}{*}{[PERSON] et al. [45]} & \multirow{6}{*}{2013} & Gaussian Mixture Model (GMM) & 100.00 \\ \cline{3-4}  & & DT & 88.90 \\ \cline{3-4}  & & Fuzzy sugeno (PS) & 90.70 \\ \cline{3-4}  & & KNN & 86.30 \\ \cline{3-4}  & & Naive Bayes classifier (NBC) & 94.4 \\ \cline{3-4}  & & Radial basis probabilistic neural & 82.4 \\ \cline{3-4}  & & network (RBPNN) & \\ \hline \hline \end{tabular}
\end{table}
Table 14: The comparison of existing machine learning performances in the detection of CAD with the use of different algorithms and medical features. The comparison results in the table showed that selection of appropriate algorithms and medical features for machine learning had a significant impact on the accuracy of CAD detection.