Early Mental Health Detection with Machine Learning : A Practical Approach to Model Development and Implementation
Abstract
Academic pressures, lifestyle changes, and socio-economic factors significantly impact mental health, a critical determinant of academic success and well-being. Early detection and intervention are crucial to mitigate severe outcomes like academic underperformance and suicidal tendencies. Leveraging tools like the DASS-42, this study examines mental health patterns using Support Vector Machine (SVM) models, achieving accuracies of 88% for depression, 71% for stress, and 57% for anxiety. While the model excels in identifying "Normal" cases, its performance for "Mild," "Moderate," and "Severe" cases highlights limitations due to class imbalance and feature representation. The findings reveal that anxiety is the most volatile and severe condition, with peaks in 2018 and 2022, while stress remains manageable and depression moderately stable. Gender and program-specific differences emphasize the need for tailored interventions. Addressing challenges related to data quality, algorithmic transparency, and ethical concerns is essential for real-world applications. This study highlights the potential of machine learning in early detection and intervention for mental health issues. Future research should explore advanced feature engineering techniques and develop more interpretable models to enhance clinical decision-making.
Keywords
References
E. Sheldon et al., “Prevalence and risk factors for mental health problems in university undergraduate students: A systematic review with meta-analysis,” Journal of Affective Disorders, vol. 287. Elsevier BV, pp. 282–292, May 2021. doi: 10.1016/j.jad.2021.03.054.
R. Winzer, L. Lindberg, K. Guldbrandsson, and A. Sidorchuk, “Effects of mental health interventions for students in higher education are sustainable over time: a systematic review and meta-analysis of randomized controlled trials,” PeerJ, vol. 6. PeerJ, p. e4598, Apr. 02, 2018. doi: 10.7717/peerj.4598.
S. Shaffique, S. S. Farooq, H. Anwar, H. M. Asif, M. Akram, and S. K. Jung, “Meta-analysis of Prevalence of Depression, Anxiety and Stress Among University Students,” RADS Journal of Biological Research & Applied Sciences, vol. 11, no. 1. Jinnah University for Women, pp. 27–32, Sep. 21, 2020. doi: 10.37962/jbas.v11i1.308.
M. Mofatteh, “Risk factors associated with stress, anxiety, and depression among university undergraduate students,” AIMS Public Health, vol. 8, no. 1. American Institute of Mathematical Sciences (AIMS), pp. 36–65, 2021. doi: 10.3934/publichealth.2021004.
A. S. Dessauvagie, H.-M. Dang, T. A. T. Nguyen, and G. Groen, “Mental Health of University Students in Southeastern Asia: A Systematic Review,” Asia Pacific Journal of Public Health, vol. 34, no. 2–3. SAGE Publications, pp. 172–181, Nov. 19, 2021. doi: 10.1177/10105395211055545.
J.-J. Chang, Y. Ji, Y.-H. Li, H.-F. Pan, and P.-Y. Su, “Prevalence of anxiety symptom and depressive symptom among college students during COVID-19 pandemic: A meta-analysis,” Journal of Affective Disorders, vol. 292. Elsevier BV, pp. 242–254, Sep. 2021. doi: 10.1016/j.jad.2021.05.109.
G. Agyapong-Opoku, B. Agyapong, G. Obuobi-Donkor, and E. Eboreime, “Depression and Anxiety among Undergraduate Health Science Students: A Scoping Review of the Literature,” Behavioral Sciences, vol. 13, no. 12. MDPI AG, p. 1002, Dec. 07, 2023. doi: 10.3390/bs13121002.
W. Li, Z. Zhao, D. Chen, Y. Peng, and Z. Lu, “Prevalence and associated factors of depression and anxiety symptoms among college students: a systematic review and meta‐analysis,” Journal of Child Psychology and Psychiatry, vol. 63, no. 11. Wiley, pp. 1222–1230, Mar. 16, 2022. doi: 10.1111/jcpp.13606.
S. Bozkurt et al., “Reporting of demographic data and representativeness in machine learning models using electronic health records,” Journal of the American Medical Informatics Association, vol. 27, no. 12. Oxford University Press (OUP), pp. 1878–1884, Sep. 16, 2020. doi: 10.1093/jamia/ocaa164.
R. Tornero-Costa, A. Martinez-Millana, N. Azzopardi-Muscat, L. Lazeri, V. Traver, and D. Novillo-Ortiz, “Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review,” JMIR Mental Health, vol. 10. JMIR Publications Inc., p. e42045, Feb. 02, 2023. doi: 10.2196/42045.
M. Bracher-Smith, K. Crawford, and V. Escott-Price, “Machine learning for genetic prediction of psychiatric disorders: a systematic review,” Molecular Psychiatry, vol. 26, no. 1. Springer Science and Business Media LLC, pp. 70–79, Jun. 26, 2020. doi: 10.1038/s41380-020-0825-2.
J. D. Morgenstern et al., “Predicting population health with machine learning: a scoping review,” BMJ Open, vol. 10, no. 10. BMJ, p. e037860, Oct. 2020. doi: 10.1136/bmjopen-2020-037860.
A. Le Glaz et al., “Machine Learning and Natural Language Processing in Mental Health: Systematic Review,” Journal of Medical Internet Research, vol. 23, no. 5. JMIR Publications Inc., p. e15708, May 04, 2021. doi: 10.2196/15708.
A. B. R. Shatte, D. M. Hutchinson, and S. J. Teague, “Machine learning in mental health: a scoping review of methods and applications,” Psychological Medicine, vol. 49, no. 09. Cambridge University Press (CUP), pp. 1426–1448, Feb. 12, 2019. doi: 10.1017/s0033291719000151.
S. Denieffe, “Commentary: Purposive sampling: complex or simple? Research case examples,” Journal of Research in Nursing, vol. 25, no. 8. SAGE Publications, pp. 662–663, Jun. 18, 2020. doi: 10.1177/1744987120928156.
H. Ames, C. Glenton, and S. Lewin, “Purposive sampling in a qualitative evidence synthesis: a worked example from a synthesis on parental perceptions of vaccination communication,” BMC Medical Research Methodology, vol. 19, no. 1. Springer Science and Business Media LLC, Jan. 31, 2019. doi: 10.1186/s12874-019-0665-4.
J. Lee, E.-H. Lee, and S. H. Moon, “Systematic review of the measurement properties of the Depression Anxiety Stress Scales–21 by applying updated COSMIN methodology,” Quality of Life Research, vol. 28, no. 9. Springer Science and Business Media LLC, pp. 2325–2339, Apr. 01, 2019. doi: 10.1007/s11136-019-02177-x.
A. Y. Yeung, L. Yuliawati, and S.-H. Cheung, “A Systematic Review and Meta-Analytic Factor Analysis of the Depression Anxiety Stress Scales.” Center for Open Science, Dec. 15, 2018. doi: 10.31234/osf.io/bzhgk.
J. Mohammadzadeh, S. Mami, and K. Omidi, “Mean Scores of Depression, Anxiety and Stress in Iranian University Students Based on DASS-21: A Systematic Review and Meta-analysis,” International Journal of Epidemiologic Research, vol. 6, no. 1. Maad Rayan Publishing Company, pp. 42–48, Jan. 24, 2019. doi: 10.15171/ijer.2019.08.
H. Alharthi, "Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college-age students using artificial intelligence technology," in Proceedings of the 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DC
M. T. Hawes, H. A. Schwartz, Y. Son, and D. Klein, "Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning," Psychological Medicine, pp. 1–7, 2022. DOI: 10.1017/S0033291722003452.
C. El Morr, M. Jammal, I. Bou-Hamad, S. Hijazi, D. Ayna, M. Romani, and R. Hoteit, "Predictive Machine Learning Models for Assessing Lebanese University Students’ Depression, Anxiety, and Stress During COVID-19," Journal of Primary Care & Community Health, vol. 15, 2024. DOI: 10.1177/21501319241235588.
R. Sánchez-Reolid, M. T. López, and A. Fernández-Caballero, "Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review," Preprints, 2020. DOI: 10.20944/preprints202011.0043.v1.
J. Chung and J. Teo, "Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges," Applied Computational Intelligence and Soft Computing, vol. 2022, 2022, pp. 1–19. DOI: 10.1155/2022/9970363.
M. P. Collins and S. E. Pape, “The potential of support vector machine as the diagnostic tool for schizophrenia: A systematic literature review of neuroimaging studies,” European Psychiatry, vol. 26, no. S2. Cambridge University Press (CUP), pp. 1363–1363, Mar. 2011. doi: 10.1016/s0924-9338(11)73068-1.
P. Timsina, O. F. El-Gayar, and J. Liu, “Leveraging Advanced Analytics Techniques for Medical Systematic Review Update,” 2015 48th Hawaii International Conference on System Sciences. IEEE, pp. 976–985, Jan. 2015. doi: 10.1109/hicss.2015.121.
E. Sheldon et al., “Prevalence and risk factors for mental health problems in university undergraduate students: A systematic review with meta-analysis,” Journal of Affective Disorders, vol. 287. Elsevier BV, pp. 282–292, May 2021. doi: 10.1016/j.jad.2021.03.054.
K. Batra, M. Sharma, R. Batra, T. P. Singh, and N. Schvaneveldt, “Assessing the Psychological Impact of COVID-19 among College Students: An Evidence of 15 Countries,” Healthcare, vol. 9, no. 2. MDPI AG, p. 222, Feb. 17, 2021. doi: 10.3390/healthcare9020222.
E. B. Davies, R. Morriss, and C. Glazebrook, “Computer-Delivered and Web-Based Interventions to Improve Depression, Anxiety, and Psychological Well-Being of University Students: A Systematic Review and Meta-Analysis,” Journal of Medical Internet Research, vol. 16, no. 5. JMIR Publications Inc., p. e130, May 16, 2014. doi: 10.2196/jmir.3142.
L. Farrer et al., “Technology-Based Interventions for Mental Health in Tertiary Students: Systematic Review,” Journal of Medical Internet Research, vol. 15, no. 5. JMIR Publications Inc., p. e101, May 27, 2013. doi: 10.2196/jmir.2639.
G. González-Valero, F. Zurita-Ortega, J. L. Ubago-Jiménez, and P. Puertas-Molero, “Use of Meditation and Cognitive Behavioral Therapies for the Treatment of Stress, Depression, and Anxiety in Students. A Systematic Review and Meta-Analysis,” International Journal of Environmental Research and Public Health, vol. 16, no. 22. MDPI AG, p. 4394, Nov. 10, 2019. doi: 10.3390/ijerph16224394.
R. Winzer, L. Lindberg, K. Guldbrandsson, and A. Sidorchuk, “Effects of mental health interventions for students in higher education are sustainable over time: a systematic review and meta-analysis of randomized controlled trials,” PeerJ, vol. 6. PeerJ, p. e4598, Apr. 02, 2018. doi: 10.7717/peerj.4598.
Refbacks
- There are currently no refbacks.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272
This work is licensed under a Creative Commons Attribution 4.0 International License.