Predicting Depression in College Students Using Dynamic‎Weighted Ensemble Learning: An Explainable Artificial Intel‎ligence Approach

  • Authors

    • Youhao Wang Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen University, Thailand
    • Lan Thi Nguyen Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen University, Thailand https://orcid.org/0000-0002-8848-2168
    • Wirapong Chansanam Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen University, Thailand https://orcid.org/0000-0001-5546-8485
    https://doi.org/10.14419/er79ae93

    Received date: October 21, 2025

    Accepted date: November 21, 2025

    Published date: November 29, 2025

  • Depression Prediction; College Student Mental Health; Machine Learning; Feature Engineering; Ensemble Learning; SHAP Analysis; Mental ‎Health In-tervention

  • 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.

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    Wang, Y., Nguyen, L. T., & Chansanam, W. (2025). Predicting Depression in College Students Using Dynamic‎Weighted Ensemble Learning: An Explainable Artificial Intel‎ligence Approach. International Journal of Basic and Applied Sciences, 14(7), 606-613. https://doi.org/10.14419/er79ae93