An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease

Introduction: Cardiovascular disease (CVD) is a leading cause of death and disability globally, with ischemia and arrhythmias being critical contributors. Ischemia, due to reduced myocardial blood flow, can lead to sudden cardiac death, while arrhythmias, marked by abnormal heart rhythms, are common in the elderly. Electrocardiography (ECG) is essential for the diagnosis of these conditions. This study aims to develop a clinically interpretable diagnostic framework for ischemia and arrhythmias using key ECG fiducial features. Methods: To develop a robust ECG-based model for predicting cardiovascular diseases, we integrated data from three well-established ECG datasets: MIT-BIH Arrhythmia, European ST-T, and Fantasia. This aggregated dataset was employed to train multiple machine learning (ML) models aimed at automatically classifying heart conditions, including arrhythmia, ischemia, and healthy states. We designed a predictive framework utilizing boosting ML algorithms, enhanced by explainable artificial intelligence (XAI) techniques, to ensure high predictive performance in model interpretation. Results: The histogram gradient boosting classifier demonstrated superior classification performance, achieving an overall accuracy of 90 % in predicting heart disease based on ECG fiducial features. The model achieved area under the curve (AUC) scores of 0.99, 0.99, and 0.89 for the healthy, ischemic, and arrhythmic classes, respectively. XAI methods revealed that ECG fiducial features, such as the P-H, R-H, RR interval, QRS duration, QT interval, and ST segment, were significant diagnostic indicators for heart disease. Conclusions: This study uses machine learning and XAI models to classify arrhythmia and ischemia from ECG data, enhancing interpretability clinical diagnostics for prevention and intervention to reduce disabilities.