Machine Learning Predicts Pollution-Linked ACS Mortality

Researchers led by Sazzli Kasim publish in Scientific Reports this week a study analyzing 14,145 Malaysian acute coronary syndrome cases from 2006–2017, combining National Cardiovascular Disease Database clinical records with daily air-quality metrics. A random forest model achieved AUC 0.843 versus TIMI’s 0.791 (STEMI) and 0.565 (NSTEMI), with net reclassification improvements of 8.71% (STEMI) and 86.94% (NSTEMI); SHAP flagged NOx and O3 as top predictors, indicating pollution-aware models improve mortality prediction and require regional validation.