## 3 Results  
Polynomial-SVM Model with Grid Search Optimization Showed the Best Performance in the Detection of CAD  
In this study, the classification performances of linear-SVM, polynomial-SVM, and RBF-SVM were first studied to choose the best SVM algorithm for the following investigation. Previous studies suggested that the SVM model could not achieve best classification outcomes if the kernel functions and parameters were not selected properly [21]. To find the best SVM algorithm for the detection of CAD, we compared the classification performances of linear-SVM, polynomial-SVM, and RBF-SVM algorithm with the use of three different parameter setting methods of default method, grid search and PSO (Table 2). The results showed that although all SVM algorithms worked well with the default parameters, the classification performances of the SVM models were improved remarkably through parameter optimizations with both grid search and PSO. Moreover, for all the SVM models, the performances optimized by grid search were better than those by PSO (Table 2). On the other hand, the classification performances of polynomial-SVM were much better than those of linear-SVM and RBF-SVM (Table 2). This suggested that polynomial-SVM with the parameter optimization method of grid search achieved the best performance among three given SVM models.  
To compare the performances among the best SVM algorithm (polynomial-SVM) and the other machine learning algorithms, the accuracies of the polynomial-SVM algorithm and other machine learning algorithms (LR, DT, LDA, k-NN, and ANN) were further studied (Table 3). The aim of this part was to assess the classification capability of all used algorithms. The results indicated that the polynomial-SVM model achieved the best performance in the detection of CAD, followed by ANN. The classification accuracy of LR, DT, LDA, k-NN, ANN, and polynomial-SVM were 96.30%, 97.00%, 92.30%, 95.70%, 98.40% and 100.00%, respectively. This suggested that polynomial-SVM was the best algorithm for the detection of CAD among all these algorithms. Hence, the following studies will be mainly based on the polynomial-SVM model (with the parameter optimization of grid search) for further research.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Results** & \multicolumn{2}{c}{**SVM Kernel Functions Methods**} & **Default** & **Grid Search** & **PSO** \\ \hline \multirow{2}{*}{Training Accuracy (\%)} & Linear & 90.86 & 96.56 & 95.24 \\  & Polynomial & 95.19 & 100 & 98.99 \\  & RBF & 88.81 & 99.77 & 97.99 \\ \hline \multirow{2}{*}{Testing Accuracy (\%)} & Linear & 90.29 & 96.92 & 94.88 \\  & Polynomial & 95.23 & 100 & 99.86 \\  & RBF & 88.05 & 99.66 & 97.65 \\ \hline \multirow{2}{*}{Testing Sensitivity (\%)} & Linear & 90.39 & 98.65 & 97.94 \\  & Polynomial & 91.64 & 100 & 99.80 \\  & RBF & 88.42 & 99.32 & 97.55 \\ \hline \multirow{2}{*}{Testing Specificity (\%)} & Linear & 87.25 & 95.10 & 91.86 \\  & Polynomial & 94.09 & 100 & 99.83 \\ \cline{1-1}  & RBF & 90.09 & 100 & 97.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of the classification performances of three different SVM models (linear-SVM, polynomial-SVM, and radial basis function (RBF)-SVM) with the use of different parameter setting methods.