Optimizing AI and Machine Learning for Early Detection of Mental Health Disorders: Comparative Analysis of Algorithms

Mental health disorders, such as stress, anxiety, and depression, have become increasingly prevalent, necessitating early detection and intervention. This study presents a machine learning-based approach to identify mental health issues by analyzing social media text data. Utilizing a dataset comprising labeled tweets, applied various classification algorithms, including Logistic Regression, Random Forest, and Support Vector Machines. The models were evaluated using metrics such as accuracy, precision, recall, and F1-score. Among the tested models, Logistic Regression achieved the highest accuracy of 94.89%, hence demonstrating its effectiveness in detecting mental health-related content. The findings underscore the potential of leveraging social media data and machine learning techniques for early identification of mental health concerns, which can aid in timely support and treatment.