Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
This addresses the lack of transparency in AI models for clinical risk stratification, potentially enhancing trust and deployment in endemic regions.
The paper tackled the problem of predicting sudden cardiac death in Chagas cardiomyopathy by applying a logic-based explainability method to an AI classifier, achieving over 95% accuracy and recall with 100% explanation fidelity.
Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. While AI and machine learning models improve risk stratification, their adoption is hindered by a lack of transparency, as they are often perceived as \textit{black boxes} with unclear decision-making processes. Some approaches apply heuristic explanations without correctness guarantees, leading to mistakes in the decision-making process. To address this, we apply a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC. This explainability method, applied to an AI classifier with over 95\% accuracy and recall, demonstrated strong predictive performance and 100\% explanation fidelity. When compared to state-of-the-art heuristic methods, it showed superior consistency and robustness. This approach enhances clinical trust, facilitates the integration of AI-driven tools into practice, and promotes large-scale deployment, particularly in endemic regions where it is most needed.