## 3 Results
### The Performance of the SVM Models Was Not Affected by Data Sampling  
To examine whether the classification performances of the polynomial-SVM model were dependent on data sampling, 75% morphometric data from both non-CAD and CAD subjects were randomly selected as the training datasets, and the other morphometric data were selected as the testing datasets. The results in Table 4 indicated that the values of the classification performances (including training accuracy, testing accuracy, testing sensitivity, and testing specificity) for the polynomial-SVM model were similar in all sampling cases. Moreover, the fluctuation of the classification performances of the polynomial-SVM model was less than 1%. This suggested that the classification performances were not affected by the data sampling. Furthermore, the results also showed that all classification performances (accuracy, sensitivity, and specificity) of the polynomial-SVM model were close to 100%. This suggested that polynomial-SVM model was very effective and stable to detect CAD by combining the measured morphological features. Table 1 also showed the comparison of the classification performances among linear-SVM, polynomial-SVM, and RBF-SVM under using different sampling methods. The fluctuations of the classification performances of all the SVM models were within 5%. This further suggested that the classification performances of all SVM algorithms were not affected by the data sampling, especially for the polynomial-SVM algorithm (see Appendix A).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Results**} & \multirow{2}{*}{**non-CAD**} & \multirow{2}{*}{**At(1,2,3)**} & \multirow{2}{*}{**At(1,2,4)**} & \multirow{2}{*}{**At(1,3,4)**} & \multirow{2}{*}{**A(2,3,4)**} & \multirow{2}{*}{**Mean**} \\ \cline{3-5}  & CAD & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Training** \\ **Accuracy** \\ **(\%)** \\ \end{tabular} } & B(1,2,3) & 100 & 100 & 100 & 100 \\  & B(1,2,4) & 100 & 100 & 100 & 100 \\  & B(1,3,4) & 100 & 100 & 100 & 100 \\  & B(2,3,4) & 100 & 100 & 100 & 100 \\  & Mean & 100 & 100 & 100 & 100 \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Testing** \\ **Accuracy** \\ **(\%)** \\ \end{tabular} } & B(1,2,3) & 100 & 100 & 100 & 100 \\  & B(1,2,4) & 100 & 100 & 99.66 & 100 & 99.92 \\  & B(1,3,4) & 100 & 100 & 100 & 100 \\  & B(2,3,4) & 100 & 100 & 100 & 100 \\  & Mean & 100 & 100 & 99.92 & 100 & 99.98 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **Testing** \\ **Sensitivity** \\ **(\%)** \\ \end{tabular} } & B(1,2,3) & 100 & 100 & 100 & 100 & 100 \\  & B(1,2,4) & 100 & 100 & 100 & 100 \\  & B(1,3,4) & 100 & 100 & 100 & 100 \\  & B(2,3,4) & 100 & 100 & 100 & 100 \\  & Mean & 100 & 100 & 100 & 100 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Specific** \\ **(\%)** \\ \end{tabular} } & B(1,2,3) & 100 & 100 & 100 & 100 & 100 \\  & B(1,2,4) & 100 & 100 & 99.31 & 100 & 99.83 \\ \cline{1-1}  & B(1,3,4) & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & B(2,3,4) & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & Mean & 100 & 100 & 99.83 & 100 & 99.96 \\ \hline \hline \end{tabular} Note: \(A(i_{1},i_{2},i_{3})\) (where \(i_{1},i_{2},i_{3}\in[1,4]\) and \(i_{1}<i_{2}<i_{3}\)) represented the features data from the non-CAD subjects; while \(B(j_{1},j_{2},j_{3})\) (where \(j_{1},j_{2},j_{3}\in[1,4]\) and \(j_{1}<j_{2}<j_{3}\)) represented the features data from the CAD subjects. The data combinations of \(A(i_{1},i_{2},i_{3})\) and \(B(j_{1},j_{2},j_{3})\) represented the training datasets.  
\end{table}
Table 3: The comparison of the classification accuracy among the polynomial-SVM model and other machine learning methods with the use of the same training datasets (75% of morphometric datasets for training).