Leveraging A Stacking Ensemble Model for Accurate Depression Prediction and Diagnosis Across All Ages

Depression is a serious mental health issue affecting people of all ages, with early detection being crucial for timely treatment. In this study, we developed a highly accurate machine-learning model using a stacking ensemble technique to predict depression. The proposed model integrates several base learners, including XGBoost, extra trees, and gradient boosting, with Random Forest as the meta-learner. By applying feature engineering, hyperparameter tuning, and balancing techniques like SMOTE, we optimized the model's performance. The final model achieved impressive performance, with accuracy, precision, recall, and F1-score all reaching 96.8%, and an AUC of 0.988. The model's average precision was also notably high at 0.990, demonstrating its effectiveness in balancing precision and recall. These results show the model's potential to greatly enhance early diagnosis and intervention for depression, offering hope for improved mental health outcomes across various age groups.