Machine Learning in the Recognition and Prognosis Prediction of Children's Post-traumatic Stress Disorder

Abstract

With the development of society, diseases have also become a topic of concern, and children's post-traumatic stress disorder has attracted more attention. This paper mainly analyzes the current situation of research on nerve protection and human brain injury at home and abroad, and designs a protection system based on machine learning model in combination with the algorithm used in this topic. First, it introduces some related concepts and theoretical foundations of this model, and studies its application in the recognition of children's post-traumatic stress disorder and prognosis prediction. Secondly, the characteristics and advantages of recognition of children's post-traumatic stress defects, problems in use; evaluation difficulties and other aspects are obtained through experimental analysis. Finally, a sample test based on training classification diagram is proposed to solve these problems with Bayesian algorithm, and the results are compared to verify its accuracy. The verification results show that the recognition rate of children's post-traumatic stress disorder in this model is very high. The prediction time and recognition time of stress disorder are very fast, which meets the recognition needs of users.