A Mobile Decision Support System for Early Prediction of Mental Health Disorders

Abstract

Mental health disorders are a significant global burden, with depression, anxiety, and schizophrenia currently the leading causes of global burden. Despite increased awareness, the current diagnostic and management practices are plagued by subjective evaluation, underreporting, and heterogeneity of diagnosis. Mobile health (mHealth) applications, though they are promising, they lack intelligent, predictive capabilities that allow for early detection and intervention. This research proposes the development of an AI-driven mobile decision support system that allows for the prediction of mental health disorders by an integration of behavioral signatures, social contacts, environmental considerations, and past health history. By the exploitation of the strengths of machine learning (ML) algorithms, the system will analyze users' real-time data to provide individualized predictions and communicate timely alerts to users and clinicians. By enhancing the accuracy of early detection, access to care, and support to clinicians in making data-driven decisions the system is likely to bridge the gap in the currently available tools in mental health. The proposed solution here is a scalable, accessible, and personalized solution for mental health care, aligned to global health priorities in mental health intervention. The study also addresses the major issues of data confidentiality, interpretability of models, and cultural acceptability for widespread adoption by AI-based mental health tools. This study champions the cause of the development of new, AIbased solutions in mental health, promising the benefits of improved outcomes and overall wellness.