## 1 Introduction  
Coronary artery disease (CAD) is one of the leading causes of death in the world [1; 2]. Early detection of CAD could save patients' lives and reduce the cost of healthcare, which is of great clinical significance. Many tools have been developed for CAD diagnosis [3; 4; 5], among which the cardiac catheterization is the most direct and reliable approach [3]. However, cardiac catheterization is costly and time-consuming, and it is also an invasive and risky surgical operation. Hence, few CAD patients prefer to choose cardiac catheterization as their diagnostic method. Therefore, finding a reliable and non-invasive method for early detection of CAD is very desirable. Although some non-invasive diagnostic approaches like coronary computed tomography angiography (coronary CTA, CCTA), echocardiography, and nuclear magnetic resonance imaging (MRI) could accurately detecting CAD, they lack the ability to detect CAD at early stage. On the other hand, these methods usually require assistance from medical experts. Thus, they have not been effectively used for the early detection of CAD. Our previous study found that the changes in the morphological features of coronary arterial trees were highly correlated to the degrees of CAD lesion (unpublished yet). Moreover, these morphological changes had probably occurred before the CAD lesion developed [6]. Machine learning methods are widely used for pattern recognition and binary classification problems which can be used to classify common diseases with positive or negative attributes [7]. Therefore, it is of great potential to build a machine learning classifier by using the coronary morphological features for the non-invasive detection of CAD at its early stages. In recent decades, various machine learning algorithms were used for quick and non-invasive CAD detection, such as k-nearest neighbors (k-NN), decision tree (DT), artificial neural network (ANN) and support vector machine (SVM) [8; 9; 10; 11]. The comparison of performances of CAD detection by current machine learning methods with the use of different algorithms and medical features is shown in Table 1 (see Appendix A). Machine learning comprised of various steps: collecting the original features data, preprocessing of the data, choosing a machine learning model, training the selected model and parameter tuning, evaluating the selected model, and finally making predictions. The features selection is the first step and plays a crucial role in machine learning, which can impact the detection accuracy of the machine learning classifier [12]. Studies suggested that many features were clinically associated with CAD and varied with the degrees of diseases, including physiological-based features such as blood pressure, blood glucose, blood lipids, age, obesity degree, and overweight [1], signal-based features such as electrocardiogram (ECG) and phonocardiogram (PCG) signals [13], computer imaging-based features such as \"gray value\" [14], and morphological features such as diameter stenosis [15]. Although all these features were potentially used in machine learning for the detection of CAD, the performance of the machine learning classifier depended on the quality of features used. Selecting the most important features could significantly improve the accuracy of the medical diagnosis. Previous studies had proposed numerous feature selection algorithms to analyze the most important features among CAD datasets. For example, [PERSON] et al. developed a signal feature selection method and obtained the accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively with the use of the dual-input neural network [13]. [PERSON] et al. proposed an unsupervised features selection method from the medical datasets and obtained relatively good diagnosis results [16]. [PERSON] et al. proposed a heterogeneous hybrid feature selection algorithm and had yielded the classification accuracy of 81.23% with SMOTE and XGBoost classifier [12].