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).

翻译：冠状动脉疾病（CAD）的诊断准确性依赖于多种因素，包括人口统计学特征、症状表现、医学检查、心电图及超声心动图数据等。在此背景下，人工智能（AI）可通过整合多维度信息，帮助临床医师在诊断早期识别高危患者。为此，机器学习算法被用于基于CAD疾病风险对患者进行分类。本研究通过开发一种数据平衡与增强方法，为这一研究领域做出贡献——该方法可在数据不平衡且样本量较小的情况下实现更精确的预测。该技术可适用于多种其他场景，尤其适用于数据采集成本高昂且样本量受限的情况。实验结果表明，我们提出的CAD预测方法平均准确率达到95.36%，优于随机森林（RF）、决策树（DT）、支持向量机（SVM）、逻辑回归（LR）及人工神经网络（ANN）。