Machine Learning based Detection of Post Traumatic Stress Disorder of Mental Health

Researchers have been driven to investigate the possible role that machine learning may play in the treatment of mental health illnesses as a result of the increasing prevalence of mental health issues and the need for more effective medical care. This article provides a comprehensive examination of the current state of the art regarding the use of a wide variety of machine learning algorithms to the process of diagnosing mental health problems. In addition to this, this study will discuss about the obstacles, restrictions, and opportunities that lie ahead in the field of machine learning when it comes to mental health. This study constructs a library of research articles and studies on machine learning approaches employed for the prediction of mental health concerns by searching credible sources. Moreover, this systematic review is conducted by using the PRISMA methodology. After the process of elimination and selection, a total of 30 unique research articles are reviewed. Schizophrenia, bipolar illness, anxiety, depression, PTSD, and other mental health issues are only some of the categories into which we’ve placed the collected research articles. This section will first analyse the findings and then reflect on the obstacles and restrictions that have been encountered by the community doing research on the application of machine learning to mental health. In addition, this study outlines the potential directions that future study and development efforts in the field of applying machine learning toward mental health may take in order to take advantage of the opportunities presented.