Detection and Prediction of Future Mental Health Disorders from Social Media Data Using Machine Learning

Purpose of this study is to descry and envisage psychological conditions from social media data. Mental health analysis is an important area where individuals express their sentiments through social media data. This study uses Natural Language Processing(NLP) and Artificial Intelligence(AI) to classify conditions similar as Depression, Anxiety, Bipolar condition, and ADHD(Attention Deficit Hyperactivity Disorder). To this end, six machine learning models including Logistic Regression, Decision Tree, Support Vector Machine( SVM), Naïve Bayes, K-Nearest Neighbor( KNN), Random Forest, were estimated and integrated using an optimized weighted mongrel ensemble approach. To enhance model robustness, hyperparameter tuning, class-weight balancing, and stratified5-foldcross-validation were employed. The framework further enables early identification of potential future mental health risks based on behavioral similarity modeling. The results indicate that optimized ensemble machine learning models can effectively support proactive digital mental healthcare and early risk assessment using social media behavioral analytics. The system processes linguistic patterns & emotional cues to provide mental health professionals with tools for Early diagnosis, Risk Assessment and for Digital healthcare reasons. The proposed model achieved an accuracy of 92.2%, demonstrating superior performance in detecting mental health disorders from social media data.