The Design of Adolescents’ Physical Health Prediction System Based on Deep Reinforcement Learning

Abstract

According to the general recognition in the first half of the last century, hypertension was not considered a kind of disease, but was regarded as a compensatory response commonly seen in the elderly, and it would not occur to younger people. Because of this erroneous cognition, many young patients fail to pay attention to their own hypertension, fail to take correct and standardized treatment, and suffer from a series of complications caused by hypertension. This article summarizes the relevant factors that affect the patient’s future blood pressure from three directions: the basic characteristics of adolescent patients, the way they lower blood pressure, and the impact of the external environment. In order to make the model better fit the continuous data in the feature set of adolescents with hypertension, the structure of the internal components of the deep confidence network is optimized. Gaussian noise is introduced into the visible and hidden layers of the internal components of the network so that the stored information of the network changes from discrete to continuous during operation and improves the prediction accuracy of the blood pressure prediction model for adolescents with hypertension.