Key result
Explainable hybrid ensemble framework achieves ~100% accuracy for ECG-based arrhythmia and disease classification.
Why the study?
Most existing deep learning models for ECG classification act as black-box systems, limiting clinical adoption due to a lack of interpretability and transparency.
Does an explainable attention-based hybrid learning framework improve the classification accuracy and interpretability of ECG-based arrhythmia and cardiovascular diseases compared to existing ML and DL approaches?
Does an explainable attention-based hybrid learning framework improve the classification accuracy and interpretability of ECG-based arrhythmia and cardiovascular diseases compared to existing ML and DL approaches?
An explainable attention-based hybrid learning framework achieves 99.53% accuracy in ECG-based arrhythmia classification while providing clinically interpretable predictions using XAI techniques.
Supports interpretable AI-ECG tools for arrhythmia detection; leaves open prospective clinical validation before practice adoption.
Electrocardiogram (ECG) analysis plays a vital role in the early diagnosis of cardiac arrhythmias and cardiovascular diseases (CVDs), which remain the leading cause of global mortality. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved automated ECG classification accuracy. However, most existing DL models function as black-box systems, limiting their adoption in clinical environments due to the lack of interpretability and transparency. This paper proposes an explainable attention-based hybrid deep learning and machine learning ensemble framework for ECG-based multi-class arrhythmia and disease classification. A CNN–BiLSTM architecture enhanced with an attention mechanism is employed for spatial–temporal feature extraction, while AdaBoost and Support Vector Machine (SVM) classifiers are integrated using a stacking ensemble strategy. Experiments conducted on the MIT-BIH Arrhythmia and PTB Diagnostic ECG datasets demonstrate an overall classification accuracy of 99.53%, outperforming existing ML and DL approaches. Explainable artificial intelligence (XAI) techniques such as Grad-CAM and SHAP are incorporated to provide clinically interpretable predictions, thereby improving trust and reliability in automated ECG diagnosis.
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Sudha et al. (2026) studied Cardiac arrhythmias and cardiovascular diseases. Explainable attention-based hybrid deep learning and machine learning ensemble framework vs. Existing ML and DL approaches was evaluated on Overall classification accuracy. An explainable attention-based hybrid ensemble framework achieved an overall classification accuracy of 99.53% for ECG-based multi-class arrhythmia and disease classification.
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