Early Detection of Mental Health Issues through Machine Learning: A Comparative Analysis of Predictive Models

Abstract Mental health is a pivotal aspect of human well-being, yet it remains under-recognized and stigmatized in many societies. This study explores the integration of artificial intelligence and machine learning techniques to enhance the early detection and management of mental health disorders. By analyzing comprehensive datasets encompassing sociodemographic, medical, and environmental factors, we developed and evaluated several predictive models. Our approach included logistic regression, K-nearest neighbors, decision trees, random forests, bagging, boosting, stacking, and neural networks. The models were trained and tested to predict mental health issues, with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that ensemble methods and neural networks outperform traditional algorithms in predicting mental health conditions, offering a promising direction for early intervention and proactive mental health management. This research highlights the potential of machine learning to transform mental health care by providing accurate, data-driven insights for early detection and intervention.