ArticleFrontiers in public health2024
Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Explainable Machine Learning for Heat-Related Illness Prediction: An XGBoost-SHAP Approach Using Korean Meteorological Data.Bioengineering (Basel, Switzerland) · 2025Article
- Prediction of respiratory diseases based on random forest model.Frontiers in public health · 2025Article
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Authors and funding
8 authors.
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Abstract
Introduction: Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention. Methods: The data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014-2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm. Results: The cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted. Discussion: The incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and
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