Therapeutic Tech: A Comparative Study of AI-Driven Mental Health Interventions

This research investigates the use of machine learning algorithms to assess mental health by analyzing a large dataset that encompasses demographic, lifestyle, and social behavior information. The study utilized a Boosting model, a Decision Tree Classifier, and a logistic regression model to evaluate model performance comprehensively. Among these, the Boosting model achieved the highest accuracy at 81.75%, followed closely by the Decision Tree Classifier at 80.69% and the logistic regression model at 79.63%. Beyond performance evaluation, the research explored feature engineering methods to improve model interpretability and pinpoint critical factors influencing mental health. To enhance model reliability and generalizability, hyperparameter tuning and cross-validation techniques were employed. The results indicate that the proposed models are capable of effectively identifying individuals at risk for various mental health issues. Insights gained from the analysis of feature importance can provide valuable recommendations for clinicians and aid in developing future behavioral medicine strategies. Moreover, AI-based models may uncover connections between lifestyle factors and elevated stress levels. This study seeks to promote the application of AI in mental health evaluation and emphasize the advantages of data-driven methods in tackling social and health challenges.