Explainable AI-Based Clinical Signal Analysis for Myocardial Infarction Classification and Risk Factor Interpretation

Abstract

Myocardial infarction (MI) remains one of the most critical causes of death worldwide, demanding predictive models that balance accuracy with clinical interpretability. This study introduces an explainable artificial intelligence (XAI) framework that integrates least absolute shrinkage and selection operator (LASSO) regression for feature selection, logistic regression for prediction, and Shapley additive explanations (SHAP) for interpretability. Using a dataset of 918 patients and 12 signal-derived clinical variables, the model achieved an accuracy of 87.7%, a recall of 0.87, and an F1 score of 0.89, confirming its robust performance. The key risk factors identified were age, fasting blood sugar, ST depression, flat ST slope, and exercise-induced angina, while the maximum heart rate and upward ST slope served as protective factors. Comparative analyses showed that the SHAP and p-value methods largely aligned, consistently highlighting ST_Slope_Flat and ExerciseAngina_Y, though discrepancies emerged for ST_Slope_Up, which showed limited statistical significance but high SHAP contribution. By combining predictive strength with transparent interpretation, this study addresses the black-box limitations of conventional models and offers actionable insights for clinicians. The findings highlight the potential of signal-driven XAI approaches to improve early detection and patient-centered prevention of MI. Future work should validate these models on larger and more diverse datasets to enhance generalizability and clinical adoption.