A Next-Generation Deep Learning Model for Early Prediction of Cardiovascular Events

Abstract

Cardiovascular diseases (CVDs) continue to be the foremost cause of mortality worldwide, emphasizing the urgent need for effective preventive diagnostic tools. Early and accurate prediction of cardiovascular events enables timely medical interventions, reduces morbidity and mortality, and assists clinicians in formulating personalized treatment plans. This research introduces Hybrid-CardioNet, a next-generation deep learning-based predictive model designed to enhance early cardiovascular event prediction through the integration of multiple learning components. The model combines Convolutional Neural Networks (CNN) for efficient spatial feature extraction, Bidirectional Long-Short Term Memory (Bi-LSTM) networks for capturing temporal dependencies across sequential clinical and physiological data, and an Attention Mechanism for prioritizing critical features influencing cardiovascular risk. For experimentation, a synthetic dataset constructed to resemble real-world patient distributions was utilized, incorporating demographic, clinical, ECG, and biochemical markers. Hybrid-CardioNet achieved superior performance with an accuracy of 96.84%, an F1-Score of 0.958, and an Area Under Curve (AUC) of 0.982, surpassing benchmark machine learning and traditional statistical models. These findings highlight the efficacy and robustness of the proposed system and demonstrate its potential utility in proactive healthcare settings, particularly for large-scale screening and automated clinical decision support to mitigate the global burden of cardiovascular diseases.