Machine learning prediction of acute myocardial infarction incidence using regional meteorological data

Background Acute myocardial infarction (AMI) is significantly influenced by meteorological conditions; however, leveraging meteorological data to predict AMI incidence remains challenging. This study aimed to analyze weather-AMI associations and construct a predictive model using machine learning. Methods We conducted a retrospective analysis of AMI patients from a regional chest pain center, coupled with local daily weather data spanning 10 years. The relationship between weather variables and daily AMI case counts was analyzed. A Random Forest model was employed to capture potential non-linear relationships. Model performance was validated using real-world data. Results Over 4,197 days (January 2013–June 2024), 11,527 AMI patients were included. Days with higher AMI incidence were characterized by lower temperatures, greater daily temperature differences ( Δ T), and reduced air speed, while exhibiting lower humidity and precipitation compared to days with fewer cases. After multivariable adjustment, daily Δ T and air speed showed statistically significant associations with increased AMI incidence ( p < 0.05), whereas other weather elements did not. In the predictive model, daily Δ T emerged as the most important factor. Validation demonstrated moderate discriminative ability, with an AUC-ROC of 0.67 (95% CI: 0.60–0.74). Sensitivity analysis using the 95th percentile as a threshold further confirmed the model's effectiveness. Conclusions This study identifies daily temperature variation ( Δ T) and air speed as significant meteorological predictors of AMI incidence. The Random Forest model effectively captured non-linear weather-AMI relationships, supports integrating weather data—particularly temperature variability—into AMI risk stratification systems. Future research should enhance predictive power by incorporating clinical and demographic variables alongside environmental factors.