Machine learning models to predict myocardial infarctions from past climatic and environmental conditions
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Published:2022-09-16
Issue:9
Volume:22
Page:3015-3039
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ISSN:1684-9981
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Container-title:Natural Hazards and Earth System Sciences
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language:en
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Short-container-title:Nat. Hazards Earth Syst. Sci.
Author:
Marien Lennart, Valizadeh Mahyar, zu Castell Wolfgang, Nam ChristineORCID, Rechid Diana, Schneider Alexandra, Meisinger Christine, Linseisen Jakob, Wolf Kathrin, Bouwer Laurens M.ORCID
Abstract
Abstract. Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, socio-demographic and behavioural 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 and 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 MIs reasonably well (adjusted R2=0.62–0.71). 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.
Funder
Helmholtz-Gemeinschaft
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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