MACHINE LEARNING-BASED MENTAL HEALTH RISK PREDICTION AND BEHAVIOURAL CLUSTERING AMONG MALAYSIAN UNDERGRADUATES
DOI:
https://doi.org/10.24191/mjoc.v11i2.12105Keywords:
Mental Health, Machine Learning, Stacked Ensemble Learning, Behavioural Clustering, Undergraduate StudentsAbstract
Mental health issues among undergraduate students have become increasingly prevalent, affecting emotional well-being, academic performance, and overall quality of life. The growing number of students experiencing stress, anxiety, and depression highlights the need for effective predictive models to support early risk identification and personalized intervention strategies. This study investigates the performance of several machine learning approaches, including Logistic Regression (LR), Random Forest (RF), Gradient Boosting, LightGBM, Extra Trees, XGBoost, Support Vector Classifier (SVC), and a stacked ensemble model for predicting mental health risk among Malaysian undergraduate students. The study utilizes a dataset comprising demographic, academic, lifestyle, and psychological assessment attributes collected through structured self-assessment questionnaires. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, and Macro F1-score metrics. Results indicate that Logistic Regression and Random Forest achieved superior predictive performance compared to other individual models and were subsequently integrated into a stacked ensemble framework to further improve classification capability. In addition, clustering analysis identified distinct behavioural characteristics among students with moderate and severe mental health risk levels. The findings highlight the potential of integrating predictive analytics and behavioural clustering in intelligent mental health support systems. This study contributes to the growing field of machine learning applications in mental health analytics and provides valuable insights for higher education institutions in supporting undergraduate well-being.
References
Beiter, R., Nash, R., McCrady, M., Rhoades, D., Linscomb, M., Clarahan, M., & Sammut, S. (2015). The prevalence and correlates of depression, anxiety, and stress in a sample of college students. Journal of Affective Disorders, 173, 90–96. https://doi.org/10.1016/j.jad .2014.10.054
Bhandari, A. (2024). Importance of feature standardization in machine learning models. Journal of Artificial Intelligence and Data Mining, 8(2), 112–120.
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https:// doi.org/10.1007/s11749-016-0481-7
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Eisenberg, D., Lipson, S. K., Heinze, J., & Zhou, S. (2023). The Healthy Minds Study: National report 2021–2022. Healthy Minds Network. https://policycommons.net/artifacts/3494971 /hms-national-report-2021-22/4295519/
El Morr, C., El Morr, H., & Chaaya, M. (2024). Predictive machine learning models for assessing depression, anxiety, and stress among university students. Journal of Primary Care & Community Health, 15, 1–14. https://doi.org/10.1177/21501319241235588
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
Foo, D. Y. X. (2025). Campus Care: Mental health assessment and support platform. GitHub. https://github.com/debbiefyx/campus-care
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. https://doi.org/10.1016/j.engappai.2022.105151
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
Hon, H., Wah Khaw, K., Chew, X., & Wong, W. (2023). Prediction of customer churn for ABC multistate bank using machine learning algorithms. Malaysian Journal of Computing, 8(2), 1602–1619. https://doi.org/10.24191/mjoc.v8i2.21393
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). Wiley.
Jacob, A., Jose, A., & Nandakumar, R. (2022). Ensemble machine learning approaches for depression prediction: A comparative study. Healthcare Analytics, 2, 100095. https://doi.org/10.1016/j.health.2022.100095
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
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. https://papers.nips.cc/paper_files/paper /2017/hash/6449f44a102fde848669 bdd9eb6b76fa-Abstract.html
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17. https://doi.org/10.1016/j.csbj.2014.11.005
Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489
Dharmarathne, G., Jayasinghe, T. N., Bogahawaththa, M., Meddage, D. P. P., & Rathnayake, U. (2024). A novel machine learning approach for diagnosing diabetes with a self-explainable interface. Healthcare Analytics, 5, 100301. https://doi.org/10.1016/j.heal th.2024.100301
Priya, A., Garg, S., & Tigga, N. P. (2020). Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science, 167, 1258–1267. https://doi.org/10.1016/j.procs.2020.03.442
Ramón-Arbués, E., Gea-Caballero, V., Granada-López, J. M., Juárez-Vela, R., Pellicer-García, B., & Antón-Solanas, I. (2020). The prevalence of depression, anxiety and stress and their associated factors in college students. International Journal of Environmental Research and Public Health, 17(19), 7001. https://doi.org/10.3390/ijerph17197001
Razavi, M., Ziyadidegan, S., Mahmoudzadeh, A., Kazeminasab, S., Baharlouei, E., Janfaza, V., Jahromi, R., & Sasangohar, F. (2024). Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review. JMIR mental health, 11, e53714. https://doi.org/10.2196/53714
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249. https://doi.org/10.1002/widm.1249
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
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Tlachac, M. L., Rundensteiner, E. A., & Choe, E. K. (2020). Automated depression severity assessment from text and speech: A review of machine learning approaches. Journal of Biomedical Informatics, 109, 103547. https://doi.org/10.1016/j.jbi.2020.103547
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19(1), 281. https://doi.org/10.1186/s12911-019-1004-8
Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. https://doi.org/ 10.1016/S0893-6080(05)80023-1
World Health Organization. (2022). Mental health: Strengthening our response. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response
Downloads
Published
Data Availability Statement
The dataset used in this study is publicly available in the Campus Care GitHub repository at https://github.com/debbiefyx/Campus-Care/tree/main. The processed data and supporting materials generated during this study are available from the corresponding author upon reasonable request.
Issue
Section
License
Copyright (c) 2026 Dr. Syela, Debbie, Mr. Gerard, Ms. Siti, Ms. Marizkays
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
