Machine Learning Prediction of Depression and Anxiety through Social Media and Psychosocial Factors: A Population Mental Health Approach

Abstract

Mental health disorders, particularly depression and anxiety, represent a growing global burden with significant social, economic, and healthcare implications. Early identification of high-risk individuals remains a critical challenge in population mental health management. The present population-based experimental study aimed to develop and evaluate machine learning models for predicting depression and anxiety using integrated clinical, behavioral, and psychosocial variables.