Mental health disorders represent a growing global health challenge, often characterized by complex, multifactorial symptoms that complicate timely diagnosis and treatment. Advances in artificial intelligence (AI), particularly in machine learning and data-driven analytics, offer promising tools for early detection and personalized interventions. This study explores predictive modeling approaches that leverage both biological markers (biomarkers) and behavioral data to enhance diagnostic accuracy for mental health conditions such as depression, anxiety, and bipolar disorder. We investigate the integration of physiological signals (e.g., EEG, heart rate variability) with behavioral indicators (e.g., speech patterns, social activity, digital footprints) using supervised learning models. The results indicate that combining multimodal datasets significantly improves the performance of AI models in classifying mental health states, achieving improved precision and sensitivity across diverse patient profiles. Our findings underscore the potential of AI-enhanced frameworks to support clinicians in objective diagnosis, individualized care planning, and early intervention strategies. Future work will focus on longitudinal validation and ethical deployment in real-world healthcare settings.
Momand et al. (Mon,) studied this question.