SavedAdd your journal

Journals / International Journal of Pure and Applied Sciences / 2026 / Volume: 12 Issue: 1

Application of machine learning techniques for multi-class classification performance on mental disorders

Ümit Can(MUNZUR ÜNİVERSİTESİ)0000-0002-8832-6317
Pages
—
Publication Date
2026-06-22
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The contemporary social framework is predicated on a fiercely competitive system, which amplifies individual pressure and subsequently elevates the incidence of mental diseases, significantly affecting public health.
📊
Key Findings & Data:The precise identification of such illnesses via artificial intelligence techniques has become critically significant.
💡
Conclusion & Impact:Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:artificial intelligence · machine learning · mental illness

Abstract

The contemporary social framework is predicated on a fiercely competitive system, which amplifies individual pressure and subsequently elevates the incidence of mental diseases, significantly affecting public health. Consequently, governmental and non-governmental groups allocate considerable resources to tackle these concerns. The precise identification of such illnesses via artificial intelligence techniques has become critically significant. These methodologies, widely used across several healthcare sectors, have been increasingly adopted in psychiatry. Evaluating psychiatric data to forecast disease accurately enables doctors to make better decisions and establishes a robust decision-support framework. This work employed prevalent machine learning techniques to identify mental illnesses utilizing a dataset with labels for seven distinct mental states—six disorders and one normative condition. The approaches were initially evaluated on an imbalanced dataset and subsequently on a balanced dataset, facilitating comparison of the outcomes. Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.

🔖

Cite This Article

Can, Ü. (2026). Application of machine learning techniques for multi-class classification performance on mental disorders. International Journal of Pure and Applied Sciences, Volume: 12 Issue: 1. https://doi.org/10.29132/ijpas.1925035

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (41)

  1. Ü. Can and F. Tuğrul, “Metasezgisel optimizasyon yöntemlerinin psikiyatrik uygulamalardaki yeri: Bibliyometrik bir analiz,” Black Sea J. Eng. Sci., vol. 8, no. 4, pp. 1140–1151, 2025. doi: 10.34248/bsengineering.1672800.
  2. National Institute of Mental Health, “Mental illness,” Available: https://www.nimh.nih.gov/health/statistics/mental-illness. Accessed: Oct. 15, 2025.
  3. World Health Organization, “Mental disorders,” Available: https://www.who.int/news-room/fact-sheets/detail/mental-disorders. Accessed: Oct. 16, 2025.
  4. S. B. Loza et al., “Depression and cognitive decline as indicators of mental health in older adults,” Arch. Gerontol. Geriatr. Plus, vol. 1, no. 4, p. 100066, 2024.
  5. J. J. Gross, H. Uusberg, and A. Uusberg, “Mental illness and well-being: An affect regulation perspective,” World Psychiatry, vol. 18, no. 2, pp. 130–139, 2019, doi: 10.1002/wps.20618.
  6. M. Rizwan et al., “Predictive analysis of psychological disorders on health,” in Predictive An-alytics of Psychological Disorders in Healthcare, Singapore: Springer, 2022, pp. 1–16.
  7. C. Hammen, “Stress and depression,” Annu. Rev. Clin. Psychol., vol. 1, no. 1, pp. 293–319, 2005, doi: 10.1146/annurev.clinpsy.1.102803.143938.
  8. M. G. Craske et al., “What is an anxiety disorder?” Focus, vol. 9, no. 3, pp. 369–388, 2011, doi: 10.1176/foc.9.3.foc369.
  9. D. S. Pine and R. G. Klein, “Anxiety disorders,” in Rutter’s Child and Adolescent Psychiatry, pp. 628–647, 2008.
  10. S. E. Swedo and L. A. Snider, “The neurobiology and treatment of obsessive compulsive dis-order,” Neurobiology of Mental Illness, pp. 628–638, 2004.
  11. R. W. Kupka et al., “Three times more days depressed than manic or hypomanic in both bipolar I and bipolar II disorder,” Bipolar Disord., vol. 9, no. 5, pp. 531–535, 2007.
  12. R. S. McIntyre et al., “Bipolar disorders,” The Lancet, vol. 396, no. 10265, pp. 1841–1856, 2020, doi: 10.1016/S0140-6736(20)31544-0.
  13. J. C. Rosen, “Body-image disorder: Definition, development, and contribution to eating dis-orders,” in The Etiology of Bulimia Nervosa, Taylor & Francis, 2013, pp. 157–177, doi: 10.1007/978-1-4615-5905-4_11,
  14. Y. Auxéméry, “Post-traumatic psychiatric disorders: PTSD is not the only diagnosis,” La Presse Médicale, vol. 47, no. 5, pp. 423–430, 2018, doi: 10.1016/j.lpm.2017.12.006.
  15. R. Yehuda et al., “Post-traumatic stress disorder,” Nat. Rev. Dis. Primers, vol. 1, no. 1, pp. 1–22, 2015, doi: 10.1038/nrdp.2015.57.
  16. P. P. Roy-Byrne, M. G. Craske, and M. B. Stein, “Panic disorder,” The Lancet, vol. 368, no. 9540, pp. 1023–1032, 2006, doi: 10.1016/S0140-6736(06)69418-X.
  17. S. Helbig-Lang et al., “The role of safety behaviors in exposure-based treatment for panic disorder and agoraphobia,” J. Anxiety Disord., vol. 28, no. 8, pp. 836–844, 2014, doi: 10.1016/j.janxdis.2014.09.010.
  18. M. B. Stein and D. J. Stein, “Social anxiety disorder,” The Lancet, vol. 371, no. 9618, pp. 1115–1125, 2008, doi: 10.1016/S0140-6736(08)60488-2.
  19. F. Leichsenring, P. Fonagy, N. Heim, O. F. Kernberg, F. Leweke, P. Luyten, et al., “Borderline personality disorder: A comprehensive review of diagnosis and clinical presentation, etiology, treatment, and current controversies,” World Psychiatry, vol. 23, no. 1, pp. 4–25, 2024, doi: 10.1002/wps.21156.
  20. C. Sharp and K. Wall, “DSM-5 level of personality functioning: Refocusing personality dis-order on what it means to be human,” Annu. Rev. Clin. Psychol., vol. 17, no. 1, pp. 313–337, 2021, doi: 10.1146/annurev-clinpsy-081219-105402.
  21. İ. Baydili, B. Tasci, and G. Tasci, “Artificial intelligence in psychiatry: A review of biological and behavioral data analyses,” Diagnostics, vol. 15, no. 4, p. 434, 2025, doi: 10.3390/diagnostics15040434.
  22. M. A. Mansoor and K. H. Ansari, “Early detection of mental health crises through artifi-cial-intelligence-powered social media analysis: A prospective observational study,” J. Pers. Med., vol. 14, no. 9, p. 958, 2024, doi: 10.3390/jpm14090958.
  23. R. Jadhav, S. Deshmukh, V. Chellwani, and H. Sachdev, “Mental disorder detection: Bipolar disorder scrutinization using machine learning,” in 2019 9th Int. Conf. Cloud Comput., Data Sci. Eng. (Confluence), 2019, pp. 304–308, doi: 10.1109/CONFLUENCE.2019.8776913.
  24. I. Elujide, S. G. Fashoto, B. Fashoto, E. Mbunge, S. O. Folorunso, and J. O. Olamijuwon, “Ap-plication of deep and machine learning techniques for multi-label classification perfor-mance on psychotic disorder diseases,” Inform. Med. Unlocked, vol. 23, p. 100545, 2021, doi: 10.1016/j.imu.2021.100545.
  25. Y. Zhang, Z. Wang, Z. Ding, Y. Tian, J. Dai, X. Shen, Y. Liu, and Y. Cao, “Employing machine learning and deep learning models for mental illness detection,” Computation, vol. 13, no. 8, p. 186, 2025, doi: 10.3390/computation13080186.
  26. S. S. Ahmad, R. Rani, I. Wattar, M. Sharma, S. Sharma, R. Nair, and B. Tiwari, “Hybrid rec-ommender system for mental illness detection in social media using deep learning tech-niques,” Comput. Intell. Neurosci., vol. 2023, p. 8110588, 2023, doi: 10.1155/2023/8110588.
  27. U. M. Haque, E. Kabir, and R. Khanam, “Detection of child depression using machine learning methods,” PLOS ONE, vol. 16, no. 12, p. e0261131, 2021, doi: 10.1371/journal.pone.0261131.
  28. K. Vaishnavi, U. N. Kamath, B. A. Rao, and N. V. S. Reddy, “Predicting mental health illness using machine learning algorithms,” J. Phys. Conf. Ser., vol. 2161, no. 1, p. 012021, 2022, doi: 10.1088/1742-6596/2161/1/012021.
  29. M. Razavi, S. Ziyadidegan, A. Mahmoudzadeh, S. Kazeminasab, E. Baharlouei, V. Janfaza, et al., “Machine learning, deep learning, and data pre-processing techniques for detecting, predicting, and monitoring stress and stress-related mental disorders: Scoping review,” JMIR Ment. Health, vol. 11, no. 1, Art. no. e53714, 2024, doi: 10.2196/53714.
  30. J. Chung and J. Teo, “Single classifier vs. ensemble machine learning approaches for mental health prediction,” Brain Inform., vol. 10, no. 1, Art. no. 1, 2023, doi: 10.1186/s40708-022-00180-6.
  31. S. Sarkar, “Sentiment analysis for mental health dataset,” Available: https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health/data. Accessed: Nov. 3, 2025.
  32. G. Coppersmith, M. Dredze, C. Harman, K. Hollingshead, and M. Mitchell, “CLPsych 2015 shared task: Depression and PTSD on Twitter,” in Proc. 2nd Workshop Comput. Linguist. Clin. Psychol.: From Linguist. Signal to Clin. Reality, 2015, pp. 31–39, doi: 10.3115/v1/W15-1204.
  33. H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, 2009, doi: 10.1109/TKDE.2008.239.
  34. Y.-T. Zhang, G. Ling, and Y.-C. Wang, “An improved TF-IDF approach for text classifica-tion,” J. Zhejiang Univ. Sci. A, vol. 6, no. 1, pp. 49–55, 2005, doi: 10.1631/jzus.2005.A0049.
  35. A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Front. Neurorobot., vol. 7, p. 21, 2013, doi: 10.3389/fnbot.2013.00021.
  36. T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, et al., “XGBoost: Extreme gradient boosting,” R package version 0.4-2, pp. 1–4, 2015.
  37. T. G. Nick and K. M. Campbell, “Logistic regression,” in Topics in Biostatistics, New York, NY: Springer, 2007, pp. 273–301.
  38. D. Dey, M. S. Haque, M. M. Islam, U. I. Aishi, S. S. Shammy, M. S. A. Mayen, et al., “The proper application of logistic regression model in complex survey data: A systematic review,” BMC Med. Res. Methodol., vol. 25, no. 1, p. 15, 2025, doi: 10.1186/s12874-024-02454-5.
  39. L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: Unbiased boosting with categorical features,” Adv. Neural Inf. Process. Syst., vol. 31, 2018.
  40. J. T. Hancock and T. M. Khoshgoftaar, “CatBoost for big data: An interdisciplinary review,” J. Big Data, vol. 7, no. 1, p. 94, 2020, doi: 10.1186/s40537-020-00369-8
  41. S. Suthaharan, “Support vector machine,” in Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Boston, MA: Springer US, 2016, pp. 207–235, doi: 10.1007/978-1-4899-7641-3_9.