Enhancing CVD Risk Prediction: Integrating ECG Signals with Conventional Models Using AI

Introduction: Non-communicable diseases (NCDs), particularly cardiovascular diseases (CVDs), have become the leading cause of mortality worldwide, with Iran exhibiting higher-than-average incidence and mortality rates. Early detection of high-risk individuals is critical, as CVD often progresses silently. Electrocardiogram (ECG) signals, when integrated with machine learning (ML), may enhance risk prediction beyond traditional models. Objective This study aimed to evaluate the predictive performance of ECG signal features for incident CVD using machine learning models in a large population-based cohort from the Tehran Lipid and Glucose Study (TLGS). Methods A total of 4,637 adults aged 40–79 years without prior CVD at baseline (2006–2008) were followed up until 2018. Baseline characteristics, laboratory measurements, and ECG signal features were collected. CVD events were defined as coronary heart disease (CHD) or stroke. A Weibull regression model assessed the association between ECG features and incident CVD, with model performance evaluated using Harrell’s C-index, Net Reclassification Index (NRI), and Integrated Discrimination Improvement (IDI). Results Over a 10-year follow-up, 483 participants (10.4%) developed CVD. The addition of ECG signal features improved risk prediction in women, increasing the Harrell’s C-index from 0.84 to 0.85 and demonstrating significant reclassification improvement (NRI: 55.7%, IDI: 2.8%). However, no meaningful improvement was observed in men. ECG-based modeling outperformed traditional risk scores, particularly for intermediate-risk categories among women. Conclusion Incorporating ECG signal features into ML-based risk models significantly enhanced CVD prediction performance in women, suggesting potential utility for improving individualized preventive strategies. Further research is warranted to refine ECG-based risk stratification tools for broader clinical application.