Establishment and analysis of core body temperature prediction model in high temperature environment based on deep learning

At present, most core body temperature predictions are made in normal or low temperature environment, with few methods for predicting core body temperature in high temperature environment. With the development of artificial intelligence, the emergence of neural networks provides new technological support for temperature prediction. This paper proposes a core body temperature prediction model for high temperature environment based on long short-term memory (LSTM) neural network using measured body temperature data from ten healthy men, and compares it with a recurrent neural network (RNN) model. The results show that compared with the RNN model, the LSTM model has higher temperature prediction accuracy, with prediction accuracy of 0.944, 0.930 and 0.913 at three environment temperatures, respectively, while the prediction accuracy of the RNN model is 0.898, 0.865 and 0.853 at three environment temperatures, respectively.