Predicting Daily Cardiovascular Emergencies Using Weather and Air Quality Data: A 23‐Year Machine‐Learning Analysis in Taiwan

Abstract Short‐term variability in weather and air quality is known to influence cardiovascular emergencies, yet its day‐to‐day predictive value at the population level remains insufficiently understood. Using 23 years of nationwide data from Taiwan (2000–2022), we evaluated how weather and air quality conditions shape daily cardiovascular disease (CVD) emergency visits across geographic regions. We first applied unsupervised learning methods, including UMAP and K‐means clustering, to 184 environmental features to identify data‐driven environmental regimes and examine the distribution of high‐risk CVD days. We then trained eight supervised learning models to predict daily CVD emergency visits and used SHAP values to interpret key predictors. Unsupervised analyses revealed consistent seasonal and pollution‐related patterns. High‐risk days tended to cluster during cool conditions accompanied by elevated air pollution, with temperatures higher than in winter but substantially greater pollution levels. This pattern was particularly evident in northern Taiwan and among populations aged 65 years and older. Air‐pollution variables produced more clearly defined high‐risk clusters than meteorological variables alone, indicating a stronger pollution‐related contribution to acute CVD risk. In the supervised framework, tree‐based ensemble models (Random Forest, LightGBM, XGBoost) achieved the best performance, with R 2 values up to 0.67 and mean absolute percentage errors of 7%–8%. Predictability was highest for elderly populations and northern Taiwan. SHAP analyses identified NO x ‐related metrics as dominant predictors of CVD emergency visits. These results demonstrate that high‐resolution environmental data, combined with machine‐learning methods, can effectively predict CVD emergencies, delineate high‐risk environmental regimes, and support region‐specific early‐warning systems centered on air‐pollution monitoring, particularly NO x .