Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values

The early and accurate detection of the onset of acute myocardial infarction (AMI) is imperative for the timely provision of medical intervention and the reduction of its mortality rate. Machine learning techniques have demonstrated great potential in aiding disease diagnosis. In this paper, we present a framework to predict the onset of AMI using 713,447 extracted ECG samples and associated auxiliary data from the longitudinal and comprehensive ECG-ViEW II database, previously unexplored in the field of machine learning in healthcare. The framework is realized with two deep learning models, a convolutional neural network (CNN) and a recurrent neural network (RNN), and a decision-tree based model, XGBoost. Synthetic minority oversampling technique (SMOTE) was utilized to address class imbalance. High prediction accuracy of 89.9%, 84.6%, 97.5% and ROC curve areas of 90.7%, 82.9%, 96.5% have been achieved for the best CNN, RNN, and XGBoost models, respectively. Shapley values were utilized to identify the features that contributed most to the classification decision with XGBoost, demonstrating the high impact of auxiliary inputs such as age and sex. This paper demonstrates the promising application of explainable machine learning in the field of cardiovascular disease prediction.