Explainable Artificial Intelligence Approach to Heart Attack Risk Prediction
Abstract
This study examines the feasibility of explainable artificial intelligence (XAI) techniques for analyzing and accurately classifying heart attack risks. Given the complexity of heart attack risk factors, traditional machine learning models often do not provide the transparency needed for clinical decision-making. This research addresses this gap by incorporating XAI techniques, specifically SHAP (SHapley Additive exPlanations), to reveal model predictions. In this retrospective study, multiple databases were searched, and data on eight risk factors of 1319 patients were obtained. Prediction models have been developed using six different machine learning algorithms for heart attack classification. In heart attack risk classification, the XGBoost (eXtreme Gradient Boosting) model achieved the best predictive values with 91.28% Accuracy, 90% Precision, 92% Recall, and 91% F1-score. In addition, the model algorithms were evaluated according to AUC, and again, the XGBoost model achieved the best result 0.91. In the Random Forest Feature importance evaluation, troponin was the most critical variable affecting the diagnosis. SHAP graphs showed that troponin (+4.19) was the most critical risk factor. This research highlights the potential of XAI to bridge the gap between complex AI models and clinical applicability and suggests that future studies move in a promising direction to refine further and validate AI-powered healthcare solutions.
Keywords
Explainable Artificial Intelligence, Heart Attack Risk Prediction, Machine Learning, XGBoost, SHAP
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