A Deep Learning-Based Predictive Framework for Early Heart Disease Risk Assessment Using Multi-Factor Clinical Data
TL;DR
CardioAttentionNet is proposed, a novel hybrid deep learning framework that integrates residual convolutional neural networks, transformer encoders with multi-head self-attention, bidirectional long short-term memory networks, and cross-attention modules for comprehensive cardiovascular risk prediction.
Abstract
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually according to the World Health Organization. Early and accurate risk assessment is critical for timely intervention and improved patient outcomes. However, existing machine learning and deep learning approaches face significant limitations including poor modeling of nonlinear relationships, inadequate feature interaction learning, lack of uncertainty quantification, class imbalance challenges, overfitting tendencies, limited interpretability, insufficient multimodal fusion capabilities, poor cross-dataset generalization, absence of attention-based learning mechanisms, and inadequate clinical explainability. To address these critical gaps, we propose CardioAttentionNet, a novel hybrid deep learning framework that integrates residual convolutional neural networks, transformer encoders with multi-head self-attention, bidirectional long short-term memory networks, and cross-attention modules for comprehensive cardiovascular risk prediction. The framework incorporates intelligent data preprocessing, hybrid feature engineering with attention-based feature selection, adaptive feature fusion with learnable weights, channel attention mechanisms, probability calibration, and explainable AI components including SHAP, LIME, and Integrated Gradients. Evaluated on a large-scale multi-source dataset comprising 75,000 samples from UCI Cleveland, Framingham Heart Study, Kaggle, and Cardiovascular Disease datasets, CardioAttentionNet achieved exceptional performance with 98.12% accuracy, 97.84% precision, 98.37% recall, 97.93% specificity, 98.10% F1-score, 0.983 AUC, 0.962 MCC, and 0.961 Cohen’s Kappa, significantly outperforming 13 baseline methods. Comprehensive ablation studies, statistical validation, and explainability analysis demonstrate the framework’s clinical utility for early heart disease risk stratification and decision support in real-world healthcare settings.