Prediction of Cognitive-Behavioral Therapy using Deep Learning for the Treatment of Adolescent Social Anxiety and Mental Health Conditions

Abstract

Cognitive-behavioral therapy is a type of psychosocial intervention which aims to reduce the mental health conditions like depression, anxiety disorder, and others. Similarly, deep learning is a type of machine learning and artificial intelligence that imitates how humans gain certain types of knowledge. In this paper, deep learning has been used to effectively alleviate teenagers' social anxiety and improve their social ability and the quality of social relations. It aims to conduct an in-depth study on the diagnosis and treatment of cognitive behavior therapy in teenagers based on deep learning. First, it constructs the cognitive behavior diagnosis and treatment evaluation system of adolescent social anxiety and divides the system function into functional, structural, and database design. Then, the correlation prediction model between cognitive behavior therapy and adolescent social anxiety is constructed based on a multiobjective evolutionary algorithm. The risk and protective factors in adolescent growth are screened from the perspectives of people, family, school, and society. The fuzzy itemset support of different factors is defined. The vector of adolescent social anxiety expression index's weight is calculated. The subjective and objective factors of social anxiety in adolescents are extracted based on the grey correlation degree. The correlation prediction model between accurate cognitive behavior therapy and adolescents' social anxiety is established to complete the prediction research, diagnosis, and treatment effect. Simulation experiments show that the proposed method has good feasibility and high prediction accuracy. It can effectively alleviate the social anxiety of teenagers.