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

Abstract

Mental health is a state of well-being where 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 stress has now become a social issue and it has the potential to create functional incapacity at work. Chronic stress may also be linked with several physiological illnesses. The purpose of this study is to review existing research of mental health outcomes where various machine learning (ML) and deep learning (DL) algorithms have been applied. Applying our exclusion and inclusion criteria, 46 articles were finally selected from the search results obtained from various research databases and repositories. This literature on ML and mental health outcomes provides an account of the state-of-the-art techniques developed and used in this domain. The review also compares and contrasts amongst various models based on deep learning that can predict a user’s mental condition 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 and diagnose mental health of any individual were discussed. From the literature survey, this is evident that the use of ML and DL in mental health has yielded a number of benefits in the areas of diagnosis, therapy, support, research and clinical administration.