  The accuracy of coronary artery disease (CAD) diagnosis is dependent on a
variety of factors, including demographic, symptom, and medical examination,
ECG, and echocardiography data, among others. In this context, artificial
intelligence (AI) can help clinicians identify high-risk patients early in the
diagnostic process, by synthesizing information from multiple factors. To this
aim, Machine Learning algorithms are used to classify patients based on their
CAD disease risk. In this study, we contribute to this research filed by
developing a methodology for balancing and augmenting data for more accurate
prediction when the data is imbalanced and the sample size is small. The
methodology can be used in a variety of other situations, particularly when
data collection is expensive and the sample size is small. The experimental
results revealed that the average accuracy of our proposed method for CAD
prediction was 95.36, and was higher than random forest (RF), decision tree
(DT), support vector machine (SVM), logistic regression (LR), and artificial
neural network (ANN).
