Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.

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-linea

SUBMITTER: Xu H 

PROVIDER: S-EPMC11299116 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature