Machine Learning for Mental Health: Assessing Teen Depression and Anxiety Risk Factors

Rapid life patterns, combined with stress and anxiety in today's world, have led to significant mental health challenges globally. Digitized, large-scale data processing enables researchers to acquire more accurate biological insights into human body attributes than conventional assessment methods. Applications increasingly use machine learning (ML) as an effective operational system, which requires evaluating large healthcare datasets. The medical field leverages ML to assess future treatment strategies for mental disorders and predict their outcomes. This paper examines various ML algorithms that can be used to detect and diagnose depression. ML-based depression identification methods are grouped into three categories: classification, deep learning, and ensemble regression methods. The depression diagnosis model relies on several processing stages before applying ML classifiers to identify depressive symptoms, often through attendance rate analysis, followed by performance metric evaluation. The study establishes potential new approaches that investigators can use to develop clinical depression screening methods.