Preprints
https://doi.org/10.5194/nhess-2021-389
https://doi.org/10.5194/nhess-2021-389

  07 Jan 2022

07 Jan 2022

Review status: this preprint is currently under review for the journal NHESS.

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

Lennart Marien1, Mahyar Valizadeh2,, Wolfgang zu Castell3, Christine Nam1, Diana Rechid1, Alexandra Schneider2, Christine Meisinger4,5, Jakob Linseisen4,5, Kathrin Wolf2,, and Laurens Bouwer1,, Lennart Marien et al.
  • 1Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095, Hamburg, Germany
  • 2Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
  • 3GFZ German Research Centre for Geosciences, Telegrafenberg, 14473, Potsdam, Germany
  • 4Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstr. 2, 86156 Augsburg
  • 5Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85754 Neuherberg
  • These authors contributed equally to this work.
  • These authors share last authorship.

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.

Lennart Marien et al.

Status: open (until 18 Feb 2022)

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Lennart Marien et al.

Lennart Marien et al.

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Short summary
Myocardial infarctions (heart attacks), are influenced by temperature extremes, and by air pollution, lack of green spaces and ageing population. In this study, we apply machine learning models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability of MI and inter-annual trends. The models allow to quantify the importance of individual factors, and can be used to project future risks.
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