Student’s Physical Health Prediction Model Based on the Deep Neural Network

Abstract

The arrival of the Internet of Things era has brought us a series of conveniences, but it is also devouring the physical and mental health of most of us. Especially students, as the future main force of the motherland, their health cannot be ignored. To more accurately and appropriately predict the physical health of students, this paper establishes a deep convolutional neural network (CNN) model and uses its own strong function mapping ability, and using the general physical index of students’ physical health to obtain the total score as the input parameter and the total score of physical health as the output parameter, so as to establish a deep CNN prediction model for students’ physical health. The model firstly overcomes the shortcoming of singleness brought by a simple NN, and it more accurately and clearly reflects the relationship between various physical measurement indicators and the overall physical health score. Secondly, the specific ROC curve and the R-P curve are obtained by comparing the traditional gray EGM prediction model. Finally, from the comparison results of the R-P curve, it can be seen that the AUC of the deep convolutional network is 0.98, while the AUC of the gray EGM prediction model is only 0.89, which shows that the data of the deep convolutional network model is more accurate.