Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy

TL;DR

This paper introduces a logic-based explainability method with correctness guarantees for AI models predicting sudden cardiac death in Chagas cardiomyopathy, improving transparency and clinical trust.

Contribution

It presents a novel explainability approach with correctness guarantees applied to high-accuracy AI models for SCD prediction, outperforming heuristic methods.

Findings

Achieved over 95% accuracy and recall in SCD prediction.

Demonstrated 100% explanation fidelity and superior robustness.

Enhanced clinical trust and potential for large-scale deployment.

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.