Machine learning-based prediction of mortality risk from air pollution-induced acute coronary syndrome in the Western Pacific region

Air pollution is a growing cardiovascular risk in Southeast Asia, particularly in the Western Pacific region where transboundary haze and urban emissions are prevalent. Despite its relevance, traditional risk scores for Acute Coronary Syndrome (ACS) often overlook environmental factors. This study aims to assess the predictive value of air pollution exposure on ACS mortality using machine learning (ML) techniques, thereby addressing this clinical-environmental data gap. We combined clinical data from the National Cardiovascular Disease Database (NCVD) of Malaysia and daily air quality data (NOx, SO 2 , O 3 , PM 10 ) from the Department of Environment (2006–2017). ML algorithms including logistic regression, random forest (RF), XGBoost, and ensemble learning were developed to predict in-hospital mortality. SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability. Model performance was compared against the conventional TIMI risk scores for STEMI and NSTEMI patients. From 14,145 ACS cases, the RF model achieved the highest AUC (0.843), outperforming TIMI scores (0.791 for STEMI, 0.565 for NSTEMI). Net Reclassification Index improvements were 8.71% (STEMI) and 86.94% (NSTEMI), both statistically significant ( p < 0.001). SHAP analysis identified NOx and O 3 , along with clinical factors like Killip class and fasting blood glucose, as top contributors to mortality prediction. Our results highlight the feasibility of combining environmental and clinical features to improve ACS mortality prediction using ML models. While the model shows strong potential for Malaysia, external validation in other Western Pacific populations is necessary before broader generalization. This framework can inform future region-specific public health interventions targeting pollution-related cardiovascular risk.