TY  - JOUR
AU  - Vu, Thien
AU  - Kokubo, Yoshihiro
AU  - Inoue, Mai
AU  - Yamamoto, Masaki
AU  - Mohsen, Attayeb
AU  - Martin-Morales, Agustin
AU  - Dawadi, Research
AU  - Inoue, Takao
AU  - Tay, Jie Ting
AU  - Yoshizaki, Mari
AU  - Watanabe, Naoki
AU  - Kuriya, Yuki
AU  - Matsumoto, Chisa
AU  - Arafa, Ahmed
AU  - Nakao, Yoko M
AU  - Kato, Yuka
AU  - Teramoto, Masayuki
AU  - Araki, Michihiro
PY  - 2025
DA  - 2025/5/12
TI  - Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study
JO  - JMIR Cardio
SP  - e68066
VL  - 9
KW  - coronary heart disease
KW  - machine learning
KW  - logistic regression
KW  - random forest
KW  - support vector machine
KW  - Extreme Gradient Boosting
KW  - Light Gradient-Boosting Machine
KW  - Shapley Additive Explanations
KW  - CHD
KW  - SVM
KW  - XGBoost
KW  - LightGBM
KW  - SHAP
AB  - 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. A data-driven approach using machine learning (ML) offers advanced techniques to analyze complex, nonlinear, and high-dimensional datasets, uncovering novel predictors of CHD that go beyond the limitations of traditional models, which rely on predefined variables. Objective: This study aims to evaluate the contribution of various risk factors to CHD, focusing on both established and novel markers using ML techniques. Methods: The study recruited 7672 participants aged 30-84 years from Suita City, Japan, between 1989 and 1999. Over an average of 15 years, participants were monitored for cardiovascular events. A total of 7260 participants and 28 variables were included in the analysis after excluding individuals with missing outcome data and eliminating unnecessary variables. Five ML models—logistic regression, random forest (RF), support vector machine, Extreme Gradient Boosting, and Light Gradient-Boosting Machine—were applied for predicting CHD incidence. Model performance was evaluated using accuracy, sensitivity, specificity, precision, area under the curve, F1-score, calibration curves, observed-to-expected ratios, and decision curve analysis. Additionally, Shapley Additive Explanations (SHAPs) were used to interpret the prediction models and understand the contribution of various risk factors to CHD. Results: Among 7260 participants, 305 (4.2%) were diagnosed with CHD. The RF model demonstrated the highest performance, with an accuracy of 0.73 (95% CI 0.64‐0.80), sensitivity of 0.74 (95% CI 0.62‐0.84), specificity of 0.72 (95% CI 0.61‐0.83), and an area under the curve of 0.73 (95% CI 0.65‐0.80). RF also showed excellent calibration, with predicted probabilities closely aligning with observed outcomes, and provided substantial net benefit across a range of risk thresholds, as demonstrated by decision curve analysis. SHAP analysis elucidated key predictors of CHD, including the intima-media thickness (IMT_cMax) of the common carotid artery, blood pressure, lipid profiles (non–high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides), and estimated glomerular filtration rate. Novel risk factors identified as significant contributors to CHD risk included lower calcium levels, elevated white blood cell counts, and body fat percentage. Furthermore, a protective effect was observed in women, suggesting the potential necessity for gender-specific risk assessment strategies in future cardiovascular health evaluations. Conclusions: We developed a model to predict CHD using ML and applied SHAP methods for interpretation. This approach highlights the multifactor nature of CHD risk evaluation, aiming to support health care professionals in identifying risk factors and formulating effective prevention strategies. 
SN  - 2561-1011
UR  - https://cardio.jmir.org/2025/1/e68066
UR  - https://doi.org/10.2196/68066
DO  - 10.2196/68066
ID  - info:doi/10.2196/68066
ER  -