Mental Health Diagnostics Using Bidirectional Encoder Representations from Transformers with Gated Recurrent Unit Based Convolutional Neural Network

Abstract

Mental health research and the brain study have taken a rapid development with advanced technologies includes the artificial Intelligence and the deep learning. This research has grown enormously to solve the mental health issues of the current generation that is affected by various factors. The approaches driven by data with certain attributes are helping to detect, diagnose and to solve the mental health disorders. Specifically, the rapidly developing discipline of precision psychiatry makes use of sophisticated computer methods to provide more individualized mental health care. This paper presents a model based on deep learning named as Bidirectional Encoder Representations from Transformers and Gated Recurrent Unit based Convolutional Neural Network (BERT and GRU based CNN). it aims to transform the landscape of mental health diagnostics through the integration of cutting-edge deep learning models. BERT model Leveraging the power of transformer focuses on developing a sophisticated system capable of accurately and efficiently diagnosing mental health disorders. Gated recurrent Unit used to analyse diverse datasets encompassing behavioural patterns, physiological signals, and contextual information, strives to provide timely and personalized insights. Finally, Convolutional neural network will detect the final mental health condition of the person by analysing all the patterns. The experimentation is done on the dataset to check the model accuracy resulted in 97%. The goal is to enhance early detection, enable targeted interventions, and ultimately improve the overall mental well-being of individuals. This paper outlines our commitment to harnessing technology for the advancement of mental health diagnostics and underscores the potential impact of this model in revolutionizing mental healthcare practices.