Examining Mental Disorder/Psychological Chaos through Various ML and DL Techniques: A Critical Review

Abstract

Mental soundness is a condition of well-being wherein a person understands his/her potential, participates in his or her community and is able to deal effectively with the challenges and obstacles of everyday life. It circumscribes how an individual thinks, feels and responds to any circumstances. Mental strain is generally recognised as a social concern, potentially leading to a functional impairment at work. Chronic stress may also be linked with several physiological illnesses. The purpose of this research stands to examine existing research analysis of mental healthiness outcomes where diverse Deep Learning (DL) and Machine learning (ML) algorithms have been applied. Applying our exclusion and inclusion criteria, 52 articles were finally selected from the search results obtained from various research databases and repositories. This literatures on ML and mental health outcomes show an insight into the avant-garde techniques developed and employed in this domain. The review also compares and contrasts amongst various deep learning techniques for predicting a person's state of mind based on different types of data such as social media data, clinical data, etc. Finally, the open issues and future challenges of utilising Deep learning algorithms to better understand as well as diagnose mental state of any individual were discussed. From the literature survey, this is evident that the use of ML and DL in mental health has yielded significant attainment mostly in the areas of diagnosis, therapy, support, research and clinical governance.