Machine learning models to predict myocardial infarctions from past climatic and environmental conditions

Abstract. Myocardial infarctions (MI) are a major cause of death worldwide, and temperature extremes, e.g., during heat waves and cold winters, may increase the risk of MI. The relationship between health impacts and climate is complex and is influenced by a multitude of climatic, environmental, socio-demographic, and behavioral factors. Here, we present a Machine Learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany between 1998 and 2015. Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2, and O3), surrounding vegetation, as well as demographic data. We tested the following ML regression algorithms: Decision Tree, Random Forest, Multi-layer Perceptron, Gradient Boosting and Ridge Regression. The models are able to predict the total annual number of MI reasonably well (adjusted R2 = 0.59 − 0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approach provides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes.