AI-Driven Mental Health Assessment: Evaluating the Efficacy of Machine Learning in Detecting Depression and Anxiety from Digital Behavioral Data

Abstract

The integration of artificial intelligence (AI) in mental health assessments has emerged as a promising approach to improve early detection and intervention against psychological disorders. This study evaluates the effectiveness of machine learning (ML) models in detecting depression and anxiety through digital behavioral data, such as social media activity, speech patterns, and biometric signals. Using both supervised and unsupervised learning techniques, we analyzed large-scale datasets to identify behavioral markers associated with mental health conditions. The findings show that ML-based models achieve high levels of accuracy in predicting depression and anxiety, surpassing traditional self-report methods in terms of sensitivity and specificity. Additionally, the study highlights ethical considerations in the application of AI in the field of mental health, including privacy issues, algorithmic bias, and clinical validation. The study contributes to the increasing scientific evidence regarding AI-based mental health assessments and confirms the importance of responsible implementation in clinical settings and digital health. Future research needs to focus on improving model interpretability, improving generalizations in diverse populations, as well as integrating AI-based assessments into real-world mental health care systems.