Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy

Vinícius P. Chagas, Luiz H. T. Viana, Mac M. da S. Carlos, João P. V. Madeiro, Roberto C. Pedrosa, Thiago Alves Rocha, Carlos H. L. Cavalcante

TL;DR

This work applies a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC, and demonstrates strong predictive performance and 100% explanation fidelity when compared to state-of-the-art heuristic methods.

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.

Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy

TL;DR

This work applies a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC, and demonstrates strong predictive performance and 100% explanation fidelity when compared to state-of-the-art heuristic methods.

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.
Paper Structure (17 sections, 5 equations, 1 figure, 5 tables)

This paper contains 17 sections, 5 equations, 1 figure, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Sudden cardiac death (SCD)
  4. Gradient Boosted Trees
  5. First-order Logic over LRA
  6. Logic-Based Explanations
  7. Methodology
  8. Data Collection Process
  9. Machine Learning Process
  10. Feature Selection
  11. Model Selection
  12. Generating Explanations
  13. Results
  14. Building the Explainer
  15. Comparing Explanations
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Example of a regression tree in XGBoost (left is the path for False and right is the path for True)