Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-3015-2022
https://doi.org/10.5194/nhess-22-3015-2022
Research article
 | 
16 Sep 2022
Research article |  | 16 Sep 2022

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

Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer

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Cited articles

Achebak, H., Devolder, D., and Ballester, J.: Trends in temperature-related age-specific and sex-specific mortality from cardiovascular diseases in Spain: a national time-series analysis, Lancet Planet. Health, 3, e297–e306, https://doi.org/10.1016/S2542-5196(19)30090-7, 2019. a
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Bayerische Landesamt für Umwelt: Lufthygienische Landesüberwachungssystem Bayern (LÜB), https://www.lfu.bayern.de/luft/immissionsmessungen/messwertarchiv/index.htm, last access: 4 September 2022a. a, b, c, d, e, f
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Short summary
Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
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