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

ABSTRACT: 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, SO₂, O₃, PM₁₀) from the Department of Environment (2006-2017). ML algorithms including logistic regression, random forest (RF), XGBoost, and ensemble learning were

SUBMITTER: Kasim S 

PROVIDER: S-EPMC12847821 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature