A Hybrid Decision Tree and Attention-Based Neural Network (DT-ATTN) Framework for Arrhythmia Detection and Multi-Class Classification

Cardiac arrhythmias are serious health hazards that need precise, understandable diagnostic instruments. This study proposes an Explainable Hybrid DT-ATTN Model that combines Decision Trees (DT) and an Attention-Based Neural Network (ATTN) to detect and classify arrhythmias using real-time Electrocardiogram (ECG) data. The Biocare ECG-1210 equipment was used to gather a dataset of 30,000 ECG signals from the Sher-i-Kashmir Institute of Medical Sciences (SKIMS), Soura, Srinagar. The Pan-Tompkins technique and Min-Max Normalisation were used for data pre-processing. The model was trained and validated using an augmented dataset of 72,000 ECG records that included five classes: normal sinus rhythm, Atrial Fibrillation, Ventricular Tachycardia, bradycardia, and Atrial Flutter. While the attention technique enhances feature extraction and enables the model to focus on key ECG patterns, the decision tree component ensures interpretability. In every class, the Hybrid DT-ATTN Model achieves an accuracy of 97%, a precision of 97.01%, a recall of 97.03%, and an F1-score of 97.39% demonstrating its outstanding performance. Additionally, SHAP and LIME were used to illustrate the model's explainability, providing doctors with clear insights into how it makes decisions. The suggested DT-ATTN framework is a dependable tool for real-time arrhythmia diagnosis, as it not only provides high diagnostic accuracy but also bridges the gap between clinical interpretability and the complexity of deep learning models. This study demonstrates how accurate and comprehensible ECG analysis, utilising hybrid AI models, can improve patient outcomes.