Modeling Mental Health: Advances in Predictive Science towards Proactive Health Care

Psychological diseases prediction refers to the proactive identification and forecasting of mental health disorders, such as depression, anxiety, and schizophrenia, in individuals based on various risk factors, behavioral patterns, and biomarkers. This allows for timely intervention, resource optimization, reduced stigma, personalized treatments, and enhanced understanding of mental health, promoting prevention and holistic well-being. In this paper, a recent systematic review of diverse methodologies is offered for predicting mental health issues. Additionally, the obstacles, constraints, and forthcoming prospects regarding the utilization of machine learning in the mental health domain are addressed. A number of relevant research articles and studies are compiled by conducting thorough searches on reputable databases. These gathered resources are then categorized according to specific mental health conditions, encompassing schizophrenia, bipolar disorder as well as Obsessive-Compulsive Disorder (OCD) detection. This discussion delves into the discoveries made and the challenges and constraints that researchers encounter when employing machine learning in the context of mental health concerns. Furthermore, specific recommendations for potential future research and development are offered in the application of machine learning within the mental health arena.