An Advanced AI Framework for Mental Health Diagnostics Using Bidirectional Encoder Representations from Transformers with Gated Recurrent Units and Convolutional Neural Networks

Abstract

Mental health research and brain study have rapidly developed with advanced technologies including artificial Intelligence and deep learning. This research has grown enormously to solve the mental health issues of the current generation that are affected by various factors. The approaches driven by data with certain attributes are helping to detect, diagnose, and solve 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 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 a transformer focuses on developing a sophisticated system capable of accurately and efficiently diagnosing mental health disorders. A gated recurrent Unit used to analyze diverse datasets encompassing behavioral patterns, physiological signals, and contextual information, strives to provide timely and personalized insights. Finally, the Convolutional neural network will detect the final mental health condition of the person by analyzing 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.