JMIR Publications

ABSTRACT

Background:

Coronary heart disease (CHD) is a major cause of morbidity and mortality worldwide. Identifying key risk factors is essential for effective risk assessment and prevention. Machine learning (ML) offers advanced methods for analyzing complex datasets, revealing novel predictors of CHD beyond traditional models.

Objective:

This study aims to evaluate the contribution of various risk factors to CHD, focusing on both established and novel markers using machine learning techniques.

Methods:

The study recruited 7,672 participants aged 30 to 84 years from Suita City, Japan, between 1989 and 1999. Over an average of 15 years, participants were monitored for cardiovascular events. Five ML models—Random Forest (RF), XGBoost, Support Vector Machine (SVM), Logistic Regression (LR), and LightGBM—were used. The optimal model was identified based on accuracy, sensitivity, specificity, and AUC. SHapley Additive exPlanations (SHAP) were then employed to explore the contribution of various risk factors to CHD.

Results:

RF achieved the highest AUC (95% CI) of 0.94 (0.93-0.96), outperforming LR, SVM, XGBoost, and LightGBM. SHAP on the best model identified the top CHD predictors. Intima-media thickness of common carotid artery (IMT_cMax) was identified as the strongest predictor of CHD, highlighting the importance of arterial health. Systolic and diastolic blood pressure, along with lipid profiles (non-HDL cholesterol, HDL cholesterol, and triglycerides), were closely associated with CHD incidence. eGFR underscored the link between renal function and CHD. Novel insights included the impact of lower calcium levels, systemic inflammation (elevated WBC counts), fructosamine levels, and obesity-related factor (body fat percentage). A protective effect in females indicated the need for sex-specific CHD management strategies.

Conclusions:

ML, particularly the RF model combined with SHAP, effectively identified key risk factors for CHD, including arterial health, blood pressure, lipid profiles, renal function, and novel markers. These findings support a multifactorial approach to CHD risk assessment.