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

التفاصيل البيبلوغرافية
العنوان: Machine learning models to predict myocardial infarctions from past climatic and environmental conditions
المؤلفون: L. Marien, M. Valizadeh, W. zu Castell, C. Nam, D. Rechid, A. Schneider, C. Meisinger, J. Linseisen, K. Wolf, L. M. Bouwer
المصدر: Natural Hazards and Earth System Sciences, Vol 22, Pp 3015-3039 (2022)
بيانات النشر: Copernicus Publications, 2022.
سنة النشر: 2022
المجموعة: LCC:Environmental technology. Sanitary engineering
LCC:Geography. Anthropology. Recreation
LCC:Environmental sciences
LCC:Geology
مصطلحات موضوعية: Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350, Geology, QE1-996.5
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1561-8633
1684-9981
Relation: https://nhess.copernicus.org/articles/22/3015/2022/nhess-22-3015-2022.pdf; https://doaj.org/toc/1561-8633; https://doaj.org/toc/1684-9981
DOI: 10.5194/nhess-22-3015-2022
URL الوصول: https://doaj.org/article/ef958a994ee6406595b6abe4b030d74f
رقم الأكسشن: edsdoj.f958a994ee6406595b6abe4b030d74f
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:15618633
16849981
DOI:10.5194/nhess-22-3015-2022