Machine learning prediction of cardiovascular disease risk progression from sulfur dioxide exposure in longitudinal population studies in China.

Cardiovascular disease (CVD) is a prevalent global health issue and one of the leading causes of death. Aging and air pollution are well-established risk factors for CVD. This study aims to investigate the association between air pollutants (sulfur dioxide, carbon monoxide, PM1, PM2.5, nitrogen dioxide, ozone) and the risk of heart disease. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) and the China High Air Pollutants (CHAP) database, we employed multivariable-adjusted logistic regression to analyze the relationship between pollutants and heart disease. Additionally, six binary classification machine learning algorithms—AdaBoost, Decision Tree, LightGBM, XGBoost, Random Forest, and GBDT—were used to construct predictive models. The models incorporated air pollutant concentrations (SO₂, CO, PM1, etc.) as core features, along with covariates such as gender, age, and hypertension. The data were split into an 80% training set and a 20% test set, with cross-validation applied to ensure robustness. Multivariable regression analysis revealed that after adjusting for multiple covariates (including BMI, blood glucose, and other pollutants), each 1-unit increase in SO₂ concentration was associated with an odds ratio (OR) of 1.040 for heart disease (95% confidence interval [CI]: 1.027–1.054, p < 0.00001). Among the machine learning models, Random Forest exhibited the best performance, with an AUC of 0.794 in the training set and 0.726 in the test set. SHAP analysis confirmed that SO₂ was the most impactful pollutant. Subgroup analysis indicated a significant interaction between SO₂ and household registration type (p < 0.05). Future research should further explore the mechanisms underlying SO₂-induced cardiac damage and optimize the applicability of predictive models.