Machine Learning in Mental Health: Bridging Gaps in Diagnosis and Intervention

Mental health disorders, such as depression, anxiety, and bipolar disorder, represent a relevant public health challenge affecting close to 970 million people globally. Classical assessments include highly subjective self-reports and clinical assessments, leading often to latency in receiving care, and inaccuracy in care provision. The ML model could avail a good outlook derived from several data sources, including electronic health records, social media information, and sensor data from wearables to improve detection and intervention approaches. This study systematically reviewed 32 seminal works of the ML application in mental health involving algorithms such as SVM, RF, CNN, and deep learning models. In the bicameral model, CNN exhibited about $99 \%$ accuracy in detecting bipolar occurrence, and another, involving RF and CNN approaches also yielded 99% precision in PTSD diagnosis. Beyond these breakthroughs, challenges concern data privacy, model interpretability, and the generalizability of the numerous populations. Addressing these through explainable AI (XAI) approaches with measurements of ethical data collection greatly facilitate contributions of ML in mental health diagnostics. The review identifies current development, limitations, and future directions for more reliable, interpretable, and scalable MLbased mental health assessments.