Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917294881374208 |
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| author | Chagas, Vinícius P. Viana, Luiz H. T. Carlos, Mac M. da S. Madeiro, João P. V. Pedrosa, Roberto C. Rocha, Thiago Alves Cavalcante, Carlos H. L. |
| author_facet | Chagas, Vinícius P. Viana, Luiz H. T. Carlos, Mac M. da S. Madeiro, João P. V. Pedrosa, Roberto C. Rocha, Thiago Alves Cavalcante, Carlos H. L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22288 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 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 Alves Cavalcante, Carlos H. L. Machine Learning Logic in Computer Science 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. |
| title | Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy |
| topic | Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2602.22288 |