ArticleFrontiers in cardiovascular medicine2026
Machine learning prediction of acute myocardial infarction incidence using regional meteorological data.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
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 ( Conclusions: This study identifies daily temperature variation (
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