An Interpretable Approach with Explainable AI for the Detection of Cardiovascular Disease

Cardiovascular disease (CVD) is one of the prominent contributors to global mortality. Early detection and precise diagnosis of the disease are critically required to reduce its impact. This paper proposes an interpretable approach using Explainable Artificial Intelligence (XAI) to detect cardiovascular disease. By combining different machine learning (ML) algorithms with XAI techniques, we aim to enhance the models' predictability and transparency. Further, we use SHAP (SHapley Additive exPlanations) to provide a human-understandable explanation of model predictions. The proposed approach emphasizes both the accuracy and interpretability of the model which enhances the model's performance and also solves the black-box problem associated with AI models. Our results demonstrate that the proposed models achieve competitive performance metrics, where ANN achieved the highest accuracy of 91%. This work highlights the importance of XAI to bridge the gap between ML models and clinical decision-making, fostering trust in AI-driven healthcare solutions for the early detection of cardiovascular disease.