A Hybrid Deep Learning Framework for Heart Disease Prediction Using Multi-Modal Clinical and ECG Data

A Hybrid Deep Learning Framework for Heart Disease Prediction Using Multi-Modal Clinical and ECG Data

A Neha Patle
M.Tech Student, Department of Computer Science and Engineering All Saints College of Technology, Bhopal, India

Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) nehapatle8852@gmail.com

B Prof. Sarwesh Site
Associate Professor, Department of Computer Science and Engineering All Saints College of Technology, Bhopal, India

Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) er.sarwesh@gmail.com

ABSTRACT

Heart disease is still a heavy hitter on the list of leading causes of death around the globe, so getting ahead of the game with early and accurate predictions is crucial for effective treatment.  Traditional diagnostic systems often put all their eggs in one basket, relying solely on clinical data or electrocardiogram (ECG) signals. This one-track approach can really throw a wrench in the works, limiting their predictive power.  To tackle this head-on, we suggest a Hybrid Deep Learning Framework for Heart Disease Prediction Using Multi-Modal Clinical and ECG Data, which weaves together cutting-edge neural architectures, attention mechanisms, and ensemble strategies to boost performance and clarity.The framework gets the ball rolling by preprocessing UCI Cleveland clinical features with a bit of normalization and encoding, while ECG signals are put through the wringer with noise removal, segmentation, and scaling.  Clinical data are shaped using machine learning classifiers and ANN, while ECG features are pulled together with CNN, BiLSTM, and Transformer layers to snag local, temporal, and contextual insights.  A Graph Neural Network (GNN) weaves together the threads of clinical and ECG features, while a cross-attention fusion module dances to the rhythm of dynamically aligning and prioritizing the key features from both modalities.  Predictions are cooked up using a stacked ensemble classifier, and model interpretability is kept in the clear through SHAP values, attention heatmaps, and graph attention visualization.The experimental evaluation reveals that the proposed method takes the cake, leaving the baseline models in the dust.  Our framework hit the nail on the head with a whopping 94.8% accuracy, 93.5% precision, 95.1% recall, and 94.3% F1-score, leaving traditional machine learning in the dust at a mere 86.5%, and unimodal deep learning methods like CNN-BiLSTM and Transformer trailing behind at 89.7% and 90.4%, respectively. Talk about raising the bar!To wrap it all up, the suggested hybrid method hits the nail on the head by blending structured clinical data with ECG signals, providing top-notch predictive accuracy and clarity that’s second to none.  This positions it as a strong contender for practical clinical decision support and the early detection of cardiovascular disease.

Keywords: Heart Disease Prediction, Hybrid Deep Learning, Multi-Modal Fusion, ECG Signals, Cross-Attention, Graph Neural Network, Explainable AI,