Predicting Depression in College Students Using Dynamic Weighted Ensemble Learning: An Explainable Artificial Intelligence Approach
Year: 2025
Type: article
Abstract: Depression among college students is a global public health challenge. Traditional screening methods often suffer from poor timeliness and low sensitivity. To address this, we constructed a Dynamic Weighted Ensemble Model (DWEM) that integrates five algorithms: CatBoost, XGBoost, LightGBM, Random Forest, and ExtraTrees, with ensemble weights optimized using the Optuna framework [1]. Employing stratified 5-fold cross-validation, the model achieved an accuracy of 94.96% ± 0.44% and an AUC of 98.95% ± 0.12%, demonstrating exceptional discriminatory performance and stability. Furthermore, explainability analysis via the SHAP framework not only identified core risk factors such as academic pressure and sleep problems but also facilitated the development of a tiered intervention strategy based on predicted probabilities and feature contributions. Our study confirms that combining advanced ensemble learning with Explainable AI (XAI) can provide a powerful tool for shifting college mental health management from a “passive response” to an “active defense” paradigm, holding significant potential for clinical translation.
DOI: 10.14419/er79ae93
Institution: Khon Kaen University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4416827417
FWCI:
Cites:
Cited by:
Related to: