Optimizing Mental Health Treatment Prediction with Hybrid Machine Learning

Predictive models for detecting mental health disorders become crucial because these disorders create global health problems that need unified intervention approaches. The increasing number of mental health conditions in workplaces has made it necessary to develop information-based detection systems that help stop the progression of these conditions. Routine clinical diagnostic practices lack both promptness and adjustable delivery of solutions thus requiring machine learning as a key component for mental health analytics. This study develops an assessment system which employs machine learning predictive models to analyze workplace mental health surveys for treatment need prediction. It uses parameters from demographics combined with behavioral elements and workplace aspects like age, gender, family history data, self-employment condition and work interference. The implemented preprocessing methods incorporated label encoding and feature scaling together with SMOTETomek in order to achieve model stability and address class imbalance issues. A comprehensive evaluation process between machine learning algorithms found their suitability for mental health treatment need prediction. The analysis included Logistic Regression as well as Support Vector Machines, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost. The SHAP values feature importance assessment established work interference and family history as the essential determinants of mental health status outcomes. An ensemble Voting Classifier was implemented to combine the strongest features of the top-performing models and reached an accuracy result of 99.6%. The second best results were obtained from XGBoost and then LightGBM with 98.7% success rate and 98.2% from CatBoost. The outcomes from this study show how hybrid machine learning algorithms optimize forecasting for mental health applications through early intervention strategies.