Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy

• URL: http://arxiv.org/abs/2602.22288v1
• Date: Wed, 25 Feb 2026 14:23:31 GMT
• Title: Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
• Abstract summary: Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas (CC) remains a significant challenge.<n>While AI and machine models improve risk stratification, their adoption is hindered by a lack of transparency.<n>We apply a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC.
• Score: 0.879504058268139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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