Key result
Integrated graph network models achieve ~93% accuracy in identifying heart disease over conventional techniques.
Why the study?
Cardiovascular diseases pose a significant global health burden, requiring advanced predictive models for early intervention and personalized care via heart disease prediction and effective ECG classification.
Does a model integrating Graph Attention Networks (GATs) with Temporal Graph Networks (TGNs) and LIME improve accuracy in identifying heart disease and ECG abnormalities compared to conventional techniques?
Does a model integrating Graph Attention Networks (GATs) with Temporal Graph Networks (TGNs) and LIME improve accuracy in identifying heart disease and ECG abnormalities compared to conventional techniques?
An integrated graph network model using GATs and TGNs with LIME achieves high accuracy and interpretability for cardiovascular disease and ECG abnormality detection.
Hypothesis-generating for AI-based heart disease detection; should not yet alter clinical workflows pending rigorous validation.
Cardiovascular diseases continue to pose a significant global health burden, necessitating the development of advanced predictive models for early intervention and personalized care. Prediction of heart disease along with effective ECG data classification is essential for developing early diagnoses and enhanced patient outcomes. This research implements a new methodology that uses Graph Attention Networks (GATs) with Temporal Graph Networks (TGNs) for predictive capability enhancement and implements LIME for interpretability improvement. The clinical data analyzes multiple patient attribute relationships using GATs, while TGNs detect ECG signal temporal features. The integrated modeling system delivers <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9 2. 7 1 %</tex> accuracy in identifying heart disease cases with 96.33 % accuracy for detecting ECG abnormalities better than the conventional techniques. Explainability techniques help improve transparency through actionable information on major risk factors and time-dependent features that affect predictions. The diagnostic accuracy becomes stronger through this approach while it supports customized healthcare measures demonstrating the potential value of graph-based models for cardiovascular disease treatment.
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Saranya et al. (2025) studied Cardiovascular diseases and ECG abnormalities. Graph Attention Networks (GATs) with Temporal Graph Networks (TGNs) and LIME vs. Conventional techniques was evaluated on Accuracy in identifying heart disease cases and detecting ECG abnormalities. An integrated modeling system using Graph Attention Networks and Temporal Graph Networks achieved 92.71% accuracy in identifying heart disease cases and 96.33% accuracy for detecting ECG abnormalities.
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