Mental health detection and anticipation analysis using Deep Learning

Abstract

There has been more research into how deep learning could help find, diagnose, and treat mental health disorders because of how common they are, how important. When applied to the study of human behaviour, deep learning techniques may reveal hitherto unexplored pathways, diagnosing mental health issues, forecasting the course of diseases, and tailoring and improving treatment plans. Despite the promising prospects for M$\mathrm{L}^{\prime}$s use in mental health, this is still a nascent area of study, and there is a wide range of complicated, intertwined problems that must be overcome in order to produce successful ML-enabled applications that can be put into reality. This paper provides an overview of the existing deep learning work on psycho-socially based mental health disorders in the computing and HCI literature and aims to identify new paths for increasing growth in this crucial area. The investigation includes a quantitative synthesis and a qualitative narrative assessment, which together revealed widespread patterns, voids, and difficulties in this field. In this paper, we have covered the history of AI, ML, and DL models for the diagnosis of mental health problems. All of these subtopics were selected because they are directly related to the main subject. One of our key goals in writing this piece was to conduct research into the field of mental health and identify the best deep learning model for early identification of mental health problems.