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

AI Framework for Early Diagnosis of Coronary Artery Disease: An  Integration of Borderline SMOTE, Autoencoders and Convolutional Neural  Networks Approach