Chapter Nine - Emotion AI: Cognitive behavioral therapy for teens having some mental health disorders

Abstract

Psychosocial treatments like cognitive-behavioral therapy have been shown to work well for a number of mental health problems, such as depression, anxiety, and others. In this study, we show how deep learning may help teens overcome social anxiety and improve their friendships and other interpersonal interactions. The project's stated goal is to use deep learning to determine if cognitive behavior therapy (CBT) is useful for treating teenagers. It creates a system for diagnosis and assessment after beginning with a categorization of treatments for social anxiety in teens based on cognitive-behavioral principles. A multiobjective evolutionary algorithm is then used to anticipate the relationship between CBT and social anxiety in teens. Individual, interpersonal, institutional, and societal perspectives are used to assess risk and protective variables in adolescent development. The support for numerous variables using fuzzy sets is explained. The teenage social anxiety scale yields a weighted vector. Social anxiety may be divided into distinct components depending on how strong the gray connection is between teenagers' subjective and objective experiences. Comprehensive prediction research, an accurate diagnosis, and successful treatment can all be accomplished by developing a hypothesis that can accurately predict the effectiveness of cognitive behavior therapy. The simulation studies show that the suggested approach is both beneficial and accurate in its predictions. This might be quite beneficial for youngsters who suffer from social anxiety. On the other hand, it may be difficult to detect early indicators of mental health problems in a young child. Because of the seriousness of their mental health problem, they may feel forced to seek the assistance of a skilled psychologist or to educate themselves on alternative stress-management approaches. Only a small percentage of people are willing to take dramatic measures that could jeopardize their interpersonal relationships. When it comes to dealing with nonlinear interactions among attributes, artificial intelligence-related solutions outperform nonartificial intelligence (AI) related methods. In this investigation on the relationship between mental health and treatment, convolutional neural networks (CNNs) and long short-term memory (LSTM), among other artificial intelligence technologies, are used. The findings suggest that CNN and LSTM algorithms may be capable of anticipating a wide range of psychotherapeutic and health-related variables. This supports the assumption that these strategies are successful at identifying people who may have mental health issues. When attempting to assess the effectiveness of psychotherapy and the patient's mental health, both the CNN and LSTM algorithms had a maximum prediction error of only 2.64%. Our data's standard deviation is roughly identical to this error rate.