A random forest model to predict heatstroke occurrence for heatwave in China

Abstract Extreme heat events have recently become more frequent and represent an increasing risk of damage to public health. However, the existing prediction of heatwave related health effects has limited representativeness and verification. Our study addressed the prediction of heatstroke occurrences based on three years' data of typical cities of hot temperature in China, and examined the importance ranks of model parameters including meteorological and socioeconomic status (SES) factors. The results show that meteorological factors contributed the most to model estimation of the parameters evaluated, and SES parameters, such as the search index, were also important indicators of heatstroke prediction. The model had a satisfying performance compared to traditional linear regression models. The model established in our study can be further applied to extreme weather-related impact research and reduce economic loss due to public health expenses.