To address the issue of predicting heat stress risks for police officers engaged in outdoor operations under high-temperature conditions, a test dataset for monitoring core temperature of police officers under different environmental working conditions, levels of physical exertion and clothing scenarios was constructed. First, features such as height, weight, age, gender, body fat percentage, physical activity ratio (PAR), clothing insulation (CI), environmental temperature and relative humidity were extracted. Then, machine learning methods, including K-nearest neighbors (KNN), random forest (RF) and gradient boosting decision trees (GBDT), were used to establish predictive models of core temperature and heat stress risk for outdoor police officers. These models were subsequently validated. The results indicate that for the predictive model of core temperature for outdoor police officers working in high-temperature environments, the goodness-of-fit measure R2 exceeds 0.9 for KNN, RF and GBDT. In terms of error, the KNN model has the smallest prediction error, with a root mean square error (RMSE) of 0.053 ℃. For the heat stress prediction model for police officers engaged in outdoor operations under high-temperature conditions, the predictive performance of RF, GBDT and KNN models is significantly better than that of other models.
