## 4 Discussion

Table 7: The comparison of the classification performances of the polynomial-SVM model for each combination with two features. The combinations of (\(n\) & \(AER\)) and (\(n\) & \(\frac{D^{3}}{D^{3}}\)) performed better than the other combinations. learning. In this study, we hence comprehensively compared the prediction performance of existing commonly used algorithms to select the best algorithm for the evaluation of the features. The SVM model is one of the most well-known supervised machine learning techniques that have been widely used in pattern recognition and binary classification problems [28]. Since the SVM algorithm was invented, a large number of studies have been done by previous researchers on its optimization [21; 29; 30]. [PERSON] et al. showed that SVM parameter optimization using grid search was powerful to improve classification accuracy [21]. Our findings showed that although SVM worked well with the default parameters, and the performances of SVM could be significantly improved by using parameter optimizations with both grid search and PSO. [PERSON] et al. found that the RBF-SVM achieved the best accuracy in the classification of fasting plasma glucose level \(\geq\) 126 mg/dL vs. fasting plasma glucose level \(<\) 126 mg/dL, and the linear-SVM performed the best in the classification of fasting plasma glucose level \(\geq\) 100 mg/dL vs. fasting plasma glucose level \(<\) 100 mg/dL by applying support vector machine modeling to predict diabetes disease [29]. The study by [PERSON] et al. revealed that the RBF-SVM was better than the polynomial-SVM for the classification task when the data set was very large [30]. In this paper, we found that the polynomial-SVM with grid search optimization method performed the best in the detection of CAD by using six measured morphological features to build the SVM classifiers (Table 2). Previous studies showed that features used for building the machine learning classifier can be extracted from the diagnostic images (such as MRI, CTA and ultrasound) by automatic algorithms [31]. However, the clinical significance of these features was unknown and difficult to be interpreted. Moreover, for a given image, automatic algorithms can generate a large number of features. It was suggested that reducing the number of features in machine learning to speed up the training was of great importance especially when dealing with large datasets [32]. However, this process may degrade the classification results. A reasonable method for feature selection was of great significance for building a high-performance classification model. There are various automatic algorithms for the feature extraction or selection, such as F-score and SVM-RFE [33; 34]. These methods were helpful to efficiently select the features for the machine learning. However, the F-score algorithm did not provide any mutual information among the features [33], while the SVM-RFE algorithm can only be used to linear kernel SVM [34]. In the present study, the six morphometric features were selected because they had exhibited great clinical significance in terms of atherosclerosis [20] or indicated by medical experts. The results showed that our features achieved high classification performances with using only six morphological features for building the machine learning models, especially for polynomial-SVM (close to 100.00%) (Tables 2-4 and Figure 3 and Table 10). This suggested that reasonable and targeted selection of features for building machine learning models could greatly improve the classification performances. To the best of our knowledge, this study was the first report that the machine learning models successfully detected CAD by using the measured morphological features from the reconstructed 3D coronary bifurcations. [PERSON] et al. found that the SVM model achieved an accuracy of 98.5% with 1358 dimensions of feature while the accuracy below 85.2% with 158 dimensions of feature, by studying the effect of feature dimension on the ability to detect email spams [35].