Explainable Dynamic Weighted Ensemble Learning for Depression Risk Stratification and Tiered Intervention in University Students
DOI:
https://doi.org/10.35877/454RI.asci4621Keywords:
Explainable Artificial Intelligence, Ensemble Learning, Depression Risk Prediction, College Student Mental Health, Decision Support SystemsAbstract
Depression among college students is a growing public health concern, with existing screening methods often limited in sensitivity, scalability, and interpretability. This study developed and validated an explainable machine learning framework for early depression risk identification and tiered intervention planning in universities. We propose a Dynamic Weighted Ensemble Model (DWEM) that integrates five tree-based algorithms, with weights optimized via Bayesian search and cost-sensitive learning. Informed by the diathesis–stress framework, features were engineered and interpreted using SHAP to provide global and local explanations. The model was evaluated using stratified five-fold cross-validation with careful control of data leakage. The DWEM achieved an accuracy of 94.96% and an AUC of 98.95%, with balanced sensitivity and specificity, outperforming both single-model benchmarks and traditional questionnaire-based screening. SHAP analysis stably identified academic performance, stress-burnout, sleep problems, and protective factors as key risk determinants. Based on these outputs, a probability-based three-tier intervention framework was designed to translate risk stratification into actionable clinical support. This study demonstrates that an optimized ensemble approach, combined with theory-driven features and robust explainability, can provide a reliable, transparent, and practical tool for scalable mental health screening, supporting a shift toward proactive, data-driven prevention and efficient resource allocation in campus settings.
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References
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631. https://doi.org/10.1145/3292500.3330701
Alom, M. S., Tomal, M. A. H., Taha, R., Parvez, S., Layek, M. A., Mohsin, M., & Talukder, M. A. (2026). An Explainable Triple?Layered Ensemble Model for Early Prediction of Suicide Risk Using Machine Learning. Engineering Reports, 8(1), e70574. https://doi.org/10.1002/eng2.70574
Alvaro, P. K., Roberts, R. M., & Harris, J. K. (2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36(7), 1059–1068. https://doi.org/10.5665/sleep.2810
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1-9. https://doi.org/10.1186/s12911-020-01332-6
Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469
Auerbach, R. P., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., ... & Kessler, R. C. (2018). WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. Journal of Abnormal Psychology, 127(7), 623–638. https://doi.org/10.1037/abn0000362
Batista, G. E. A. P., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29. https://doi.org/10.1145/1007730.1007735
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., ... & Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154-170. https://doi.org/10.1002/wps.20882
Chen, S., Wang, Y., She, R., Lau, J. T. F., Mo, P. K. H., Li, J., & Li, L. (2024). Machine Learning Techniques to Predict Mental Health Problems Using Annual Student Health Survey Data: Algorithm Development and Validation Study. JMIR Mental Health, 11, e50179. https://doi.org/10.2196/50179
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
Cohen, S., Gottlieb, B. H., & Underwood, L. G. (2000). Social relationships and health. In S. Cohen, L. G. Underwood, & B. H. Gottlieb (Eds.), Social support measurement and intervention (pp. 3–25). Oxford University Press.
Dietterich, T. G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, 1–15. https://doi.org/10.1007/3-540-45014-9_1
Eisenberg, D., Golberstein, E., & Gollust, S. E. (2011). Help-seeking and access to mental health care in a university student population. Medical Care, 45(7), 594–601. https://doi.org/10.1097/MLR.0b013e31803bb4c1
Erikson, E. H. (1968). Identity: Youth and crisis. Norton.
GBD Mental Disorders Collaborators. (2022). Global burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9(2), 137–150. https://doi.org/10.1016/S2215-0366(21)00395-3
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2021). Artificial intelligence for mental health and mental illnesses: an overview. Current Psychiatry Reports, 23(11), 1-10. https://doi.org/10.1007/s11920-021-01290-6
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
Huang, Y., Yang, Z., & Li, X. (2022). Predicting depression in college students using machine learning: A systematic review and meta-analysis. Journal of Affective Disorders, 314, 236-248. https://doi.org/10.1016/j.jad.2022.07.015
Imans, D., Abuhmed, T., Alharbi, M., & El-Sappagh, S. (2024). Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment. Diagnostics, 14(21), 2385. https://doi.org/10.3390/diagnostics14212385
Jacka, F. N., O'Neil, A., Opie, R., Itsiopoulos, C., Cotton, S., Mohebbi, M., ... & Berk, M. (2017). A randomised controlled trial of dietary improvement for adults with major depression (the 'SMILES' trial). BMC Medicine, 15(1), 23. https://doi.org/10.1186/s12916-017-0791-y
Jacob, N., Lannin, D., & Vogel, D. (2022). Bridging the gap between machine learning and clinical practice in suicide prediction. Nature Human Behaviour, 6(7), 901-902. https://doi.org/10.1038/s41562-022-01362-2
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer. https://doi.org/10.1007/978-1-4614-7138-7
Kaur, P., Singh, M., & Josan, G. S. (2022). A systematic review of ensemble learning approaches for depression detection. Neuroscience Informatics, 2(4), 100075. https://doi.org/10.1016/j.neuri.2022.100075
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Levis, B., Benedetti, A., & Thombs, B. D. (2019). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening depression: A systematic review and individual participant data meta-analysis. BMJ, 365, l1476. https://doi.org/10.1136/bmj.l1476
Librenza-Garcia, D., Passos, I. C., Feiten, J. G., Lotufo, P. A., Goulart, A. C., Souza, D. S., ... & Bressan, R. A. (2021). Prediction of suicide attempts in a prospective cohort study of youth. Journal of Affective Disorders, 294, 85–90. https://doi.org/10.1016/j.jad.2021.06.063
Lian, X., Xie, Z., Zhang, Y., & Wang, Q. (2023). Challenges and strategies for implementing AI-based mental health screening in universities: A qualitative study. JMIR Mental Health, 10, e45678. https://doi.org/10.2196/45678
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018
Liu, D., Chen, Z., Marrero, W. J., Jacobson, N. C., & Thesen, T. (2023). Explainable machine learning-based prediction of depression severity in medical students. medRxiv, 2023-12. https://doi.org/10.1101/2023.12.14.23299975
López Steinmetz, L. C., Sison, M., Zhumagambetov, R., Godoy, J. C., & Haufe, S. (2024). Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine. medRxiv. https://doi.org/10.1101/2024.01.25.24301772
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
Monroe, S. M., & Simons, A. D. (1991). Diathesis-stress theories in the context of life stress research: Implications for the depressive disorders. Psychological Bulletin, 110(3), 406–425. https://doi.org/10.1037/0033-2909.110.3.406
Mumenin, N., Yousuf, M. A., Alassafi, M. O., Monowar, M. M., & Hamid, M. A. (2025). DDNet: A robust, and reliable hybrid machine learning model for effective detection of depression among university students. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3552041
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31, 6638–6648.
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426-1448. https://doi.org/10.1017/S0033291719000151
Smith, K. J., Gavey, S., Riddell, N. E., Kontari, P., & Victor, C. (2020). The association between loneliness, social isolation and inflammation: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 112, 519–541. https://doi.org/10.1016/j.neubiorev.2020.02.002
World Health Organization. (2021). Suicide worldwide in 2019: Global health estimates. World Health Organization. https://www.who.int/publications/i/item/9789240026643
World Health Organization. (2023). Depression. Fact sheet. https://www.who.int/news-room/fact-sheets/detail/depression
Wu, L., & Wang, Y. (2024). Addressing the mental health care gap in Chinese universities: A call for digital solutions. Current Psychology, 43(5), 4521-4532. https://doi.org/10.1007/s12144-023-04622-0
Zhai, Y., Zhang, Y., Chu, Z., Geng, B., Almaawali, M., Fulmer, R., ... & Du, X. (2025). Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students. Journal of Counseling & Development, 103(1), 110-125. https://doi.org/10.1002/jcad.12543
Alom, M. S., Tomal, M. A. H., Taha, R., Parvez, S., Layek, M. A., Mohsin, M., & Talukder, M. A. (2026). An Explainable Triple?Layered Ensemble Model for Early Prediction of Suicide Risk Using Machine Learning. Engineering Reports, 8(1), e70574. https://doi.org/10.1002/eng2.70574
Alvaro, P. K., Roberts, R. M., & Harris, J. K. (2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36(7), 1059–1068. https://doi.org/10.5665/sleep.2810
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1-9. https://doi.org/10.1186/s12911-020-01332-6
Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469
Auerbach, R. P., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., ... & Kessler, R. C. (2018). WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. Journal of Abnormal Psychology, 127(7), 623–638. https://doi.org/10.1037/abn0000362
Batista, G. E. A. P., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29. https://doi.org/10.1145/1007730.1007735
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., ... & Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154-170. https://doi.org/10.1002/wps.20882
Chen, S., Wang, Y., She, R., Lau, J. T. F., Mo, P. K. H., Li, J., & Li, L. (2024). Machine Learning Techniques to Predict Mental Health Problems Using Annual Student Health Survey Data: Algorithm Development and Validation Study. JMIR Mental Health, 11, e50179. https://doi.org/10.2196/50179
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
Cohen, S., Gottlieb, B. H., & Underwood, L. G. (2000). Social relationships and health. In S. Cohen, L. G. Underwood, & B. H. Gottlieb (Eds.), Social support measurement and intervention (pp. 3–25). Oxford University Press.
Dietterich, T. G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, 1–15. https://doi.org/10.1007/3-540-45014-9_1
Eisenberg, D., Golberstein, E., & Gollust, S. E. (2011). Help-seeking and access to mental health care in a university student population. Medical Care, 45(7), 594–601. https://doi.org/10.1097/MLR.0b013e31803bb4c1
Erikson, E. H. (1968). Identity: Youth and crisis. Norton.
GBD Mental Disorders Collaborators. (2022). Global burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9(2), 137–150. https://doi.org/10.1016/S2215-0366(21)00395-3
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2021). Artificial intelligence for mental health and mental illnesses: an overview. Current Psychiatry Reports, 23(11), 1-10. https://doi.org/10.1007/s11920-021-01290-6
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
Huang, Y., Yang, Z., & Li, X. (2022). Predicting depression in college students using machine learning: A systematic review and meta-analysis. Journal of Affective Disorders, 314, 236-248. https://doi.org/10.1016/j.jad.2022.07.015
Imans, D., Abuhmed, T., Alharbi, M., & El-Sappagh, S. (2024). Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment. Diagnostics, 14(21), 2385. https://doi.org/10.3390/diagnostics14212385
Jacka, F. N., O'Neil, A., Opie, R., Itsiopoulos, C., Cotton, S., Mohebbi, M., ... & Berk, M. (2017). A randomised controlled trial of dietary improvement for adults with major depression (the 'SMILES' trial). BMC Medicine, 15(1), 23. https://doi.org/10.1186/s12916-017-0791-y
Jacob, N., Lannin, D., & Vogel, D. (2022). Bridging the gap between machine learning and clinical practice in suicide prediction. Nature Human Behaviour, 6(7), 901-902. https://doi.org/10.1038/s41562-022-01362-2
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer. https://doi.org/10.1007/978-1-4614-7138-7
Kaur, P., Singh, M., & Josan, G. S. (2022). A systematic review of ensemble learning approaches for depression detection. Neuroscience Informatics, 2(4), 100075. https://doi.org/10.1016/j.neuri.2022.100075
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Levis, B., Benedetti, A., & Thombs, B. D. (2019). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening depression: A systematic review and individual participant data meta-analysis. BMJ, 365, l1476. https://doi.org/10.1136/bmj.l1476
Librenza-Garcia, D., Passos, I. C., Feiten, J. G., Lotufo, P. A., Goulart, A. C., Souza, D. S., ... & Bressan, R. A. (2021). Prediction of suicide attempts in a prospective cohort study of youth. Journal of Affective Disorders, 294, 85–90. https://doi.org/10.1016/j.jad.2021.06.063
Lian, X., Xie, Z., Zhang, Y., & Wang, Q. (2023). Challenges and strategies for implementing AI-based mental health screening in universities: A qualitative study. JMIR Mental Health, 10, e45678. https://doi.org/10.2196/45678
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018
Liu, D., Chen, Z., Marrero, W. J., Jacobson, N. C., & Thesen, T. (2023). Explainable machine learning-based prediction of depression severity in medical students. medRxiv, 2023-12. https://doi.org/10.1101/2023.12.14.23299975
López Steinmetz, L. C., Sison, M., Zhumagambetov, R., Godoy, J. C., & Haufe, S. (2024). Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine. medRxiv. https://doi.org/10.1101/2024.01.25.24301772
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
Monroe, S. M., & Simons, A. D. (1991). Diathesis-stress theories in the context of life stress research: Implications for the depressive disorders. Psychological Bulletin, 110(3), 406–425. https://doi.org/10.1037/0033-2909.110.3.406
Mumenin, N., Yousuf, M. A., Alassafi, M. O., Monowar, M. M., & Hamid, M. A. (2025). DDNet: A robust, and reliable hybrid machine learning model for effective detection of depression among university students. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3552041
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31, 6638–6648.
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426-1448. https://doi.org/10.1017/S0033291719000151
Smith, K. J., Gavey, S., Riddell, N. E., Kontari, P., & Victor, C. (2020). The association between loneliness, social isolation and inflammation: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 112, 519–541. https://doi.org/10.1016/j.neubiorev.2020.02.002
World Health Organization. (2021). Suicide worldwide in 2019: Global health estimates. World Health Organization. https://www.who.int/publications/i/item/9789240026643
World Health Organization. (2023). Depression. Fact sheet. https://www.who.int/news-room/fact-sheets/detail/depression
Wu, L., & Wang, Y. (2024). Addressing the mental health care gap in Chinese universities: A call for digital solutions. Current Psychology, 43(5), 4521-4532. https://doi.org/10.1007/s12144-023-04622-0
Zhai, Y., Zhang, Y., Chu, Z., Geng, B., Almaawali, M., Fulmer, R., ... & Du, X. (2025). Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students. Journal of Counseling & Development, 103(1), 110-125. https://doi.org/10.1002/jcad.12543
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2026-04-30
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Wang, Y., Chansanam, W., & Nguyen, L. T. (2026). Explainable Dynamic Weighted Ensemble Learning for Depression Risk Stratification and Tiered Intervention in University Students. Journal of Applied Science, Engineering, Technology, and Education, 8(1), 86–101. https://doi.org/10.35877/454RI.asci4621
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Copyright (c) 2026 Youhao Wang, Wirapong Chansanam, Lan Thi Nguyen (Author)
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