## 3 Results
### Adeuate Training Data Volume Was Necessary and Sufficient to Obtain High Detecting Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case 1**} & \multirow{2}{*}{**Results**} & \multirow{2}{*}{**CAD**} & \multirow{2}{*}{**non-CAD**} & \multirow{2}{*}{**C1**} & \multirow{2}{*}{**C2**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Case 2**} & \multirow{2}{*}{**Results**} & \multirow{2}{*}{**CAD**} & \multirow{2}{*}{**non-CAD**} & \multirow{2}{*}{**AI**} & \multirow{2}{*}{**A2**} & \multirow{2}{*}{**Mean**} \\ \hline \multirow{4}{*}{**SVM**} & Training & D1 & 100 & 100 & 100 & & & Training & B1 & 100 & 100 & 100 \\  & Accuracy & D2 & 100 & 100 & 100 & Accuracy & B2 & 100 & 100 & 100 \\  & (\%) & Mean & 100 & 100 & 100 & & & (\%) & Mean & 100 & 100 \\ \hline \multirow{4}{*}{**SVM**} & Testing & D1 & 99.83 & 100 & 99.92 & & & Testing & B1 & 84.88 & 97.59 & 91.24 \\  & Accuracy & D2 & 100 & 100 & 100 & Accuracy & B2 & 87.29 & 92.78 & 90.04 \\  & (\%) & Mean & 99.92 & 100 & 99.96 & & & Mean & 86.09 & 95.19 & 90.64 \\ \hline \multirow{4}{*}{**SVM**} & Testing & D1 & 99.66 & 100 & 99.83 & & Testing & B1 & 73.65 & 96.62 & 85.14 \\  & Sensitivity & D2 & 100 & 100 & 100 & Sensitivity & B2 & 81.08 & 92.57 & 86.83 \\  & (\%) & Mean & 99.83 & 100 & 99.92 & & & Mean & 77.37 & 94.60 & 85.98 \\ \cline{1-1} \cline{2-10}  & Testing & D1 & 100 & 100 & 100 & & & Testing & B1 & 96.50 & 98.60 & 97.55 \\ \cline{1-1}  & Specificity & D2 & 100 & 100 & 100 & & & & B2 & 93.71 & 93.01 & 93.36 \\ \cline{1-1}  & (\%) & Mean & 100 & 100 & 100 & & & & Mean & 95.11 & 95.81 & 95.46 \\ \hline \hline \end{tabular} Note: _Case 1_ and _Case 2_ indicated the 50% and 25% volume of morphometric data for training, respectively.  
\end{table} 
Table 5: The classification performances of the polynomial-SVM model with the use of different training data volumes. Bifurcation Diameter Exponent (\(n\)) and Area Expansion Ratio (\(AER\)) Were Two Key Features for the CAD Detection  
To find out which morphological feature(s) was the most important feature for the detection of CAD, we compared the classification performances of the polynomial-SVM model among each single morphological feature (Table 6). The results showed that the morphological feature \(n\) exhibited the best performance among the six morphological features, followed by \(AER\), while the \(\frac{D_{1}^{3}}{D_{m}^{3}}\) had the worst performance. Moreover, it was worth noting that, in the cases of combinations of two input features, there were two points of the classification performances that were significantly higher than the other points (see Figure 3). This suggested that those two detection results of the polynomial-SVM model achieved pretty good performances than the other results under the two-dimensional combination. Hence, in order to find out which features were selected of these two detections, we further compared the classification performances of the polynomial-SVM model for the combinations with two features (Table 7). The results showed that the combinations of (\(n\) & \(AER\)) and (\(n\) & \(\frac{D_{1}^{3}}{D_{m}^{3}}\)) achieved the testing accuracy of 89.31% and 87.93%, which were significantly higher than the third-ranked combination of \(n\) & \(\frac{D_{1}^{3}}{D_{m}^{3}}\) (with the testing accuracy of 64.83%) (Table 7). These results suggested that the polynomial-SVM model with the use of feature \(n\) and \(AER\) may have great clinical application prospects in the early detection of CAD. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Training Accuracy (\%)** & **Testing Accuracy (\%)** & **Testing Sensitivity (\%)** & **Testing Specificity (\%)** \\ \hline \(n\) & 61.63 & 62.41 & 48.65 & 76.76 \\ \(AER\) & 54.75 & 55.52 & 75.00 & 35.21 \\ \(a\) & 52.69 & 55.17 & 77.03 & 32.39 \\ \(\frac{D_{1}^{3}}{D_{1}^{3}}\) & 51.89 & 51.38 & 33.78 & 69.72 \\ \(\frac{D_{1}^{3}}{D_{1}^{3}}\) & 49.94 & 47.59 & 33.78 & 69.73 \\ \(\frac{D_{1}^{3}}{D_{1}^{3}}\) & 40.09 & 38.28 & 17.57 & 58.86 \\ \hline \hline \end{tabular}
\end{table}