Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy - Chagas, Vinícius P.; Viana, Luiz H. T.; Carlos, Mac M. da S.; Madeiro, João P. V.; Pedrosa, Roberto C.; Rocha, Thiago A.; Cavalcante, Carlos H. L. (Anais da Brazilian Conference on Intelligent Systems (BRACIS), 2025)
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 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.