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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper ID:IJSREM64382

Mental health prediction using large language models

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64382

Volume:

10 |

5 |

May 2026

Pages:

1-10

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64382

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

BPC

Binu P Chacko

Department of Computer Science

Prajyoti Niketan College, Pudukad

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Abstract

Mental health is a complex disease involving numerous individual, socioeconomic, and clinical variables. Mental illness is a health problem that undoubtedly impacts emotions, reasoning, and social interaction of a person. For children, their ability to learn could be interfered by mental disorders. Early detection of mental health issues is an essential procedure. Traditionally, mental health professionals use face-to-face interviews, self-reporting and questionnaire distribution to distinguish between the mental health status of individuals. Data preprocessing is the first step to be performed after collecting the data. It is used to clean and normalise the data before fitting it into a model. It is necessary because unprocessed raw data may contain noise, missing values, and duplicates that could affect the model's accuracy. The next step is to collect the features and select the most discriminate set to identify the disease. For the purpose of mental health prediction, approaches based on machine learning, deep learning and large language models are explored. Commonly used datasets in this domain are also discussed.

KEYWORDS

Mental health prediction Datasets Machine learning Deep learning LLM

Cite This Article

Binu P Chacko, "Mental health prediction using large language models," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-10, May 2026. https://doi.org/10.55041/IJSREM64382

Full Article/Conclusion

Mental health is widely discussed topic in every community. Early detection of mental illness is very essential as it will severely affect the individual, family and society. Apart from the traditional method of collecting information through questionnaire, symptoms can be detected through the online text posted by the patient. In this instinct, advances in AI can be utilized for mental health prediction. The different techniques in AI are best utilized for this purpose. The large language models are now capable of analyzing the contents of social media post. The recommendation model can make use of this information and predict the current mental status of the individual. In the future, it is planned to do mental health prediction using multimodal data.

References

[1]  Chung, Jetli, and Jason Teo. "Mental health prediction using machine learning: taxonomy, applications, and challenges." Applied Computational Intelligence and Soft Computing 2022.1 (2022): 9970363. [2]  Mohamed, E. Syed, et al. "A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms." Healthcare Analytics 3 (2023): 100185. [3]  Mutalib, Sofianita, Nor Safika Mohd Shafiee, and Shuzlina Abdul-Rahman. "Mental health prediction models using machine learning in higher education institution." Turkish Journal of Computer and Mathematics Education 12.5 (2021): 1782-1792. [4]  S. Graham, C. Depp, E. E. Lee, C. Nebeker, X. Tu, H.-C. Kim, and D. V. Jeste, “Artificial intelli gence for mental health and mental illnesses: an overview,” Current psychiatry reports, vol. 21, no. 11, pp. 1–18, 2019. [5]  S. Madan, F. J. Zimmer, H. Balabin, S. Schaaf, H. Fröhlich, J. Fluck, I. Neuner, K. Math iak, M. Hofmann-Apitius, and P. Sarkheil, “Deep learning-based detection of psychiatric at tributes from germanmentalhealthrecords,” International Journal of Medical Informatics, vol. 161, p. 104724, 2022. [6]  Ogunseye, Elizabeth Oluyemisi, et al. "Predictive analysis of mental health conditions using AdaBoost algorithm." ParadigmPlus 3.2 (2022): 11-26. [7]  Fudholi, Dhomas Hatta. "Mental health prediction model on social media data using CNN-BILSTM." Kinetik: Game technology, information system, computer network, computing, electronics, and control (2024): 29-44. [8]  Joanna C Yau, Benjamin Girault, Tiantian Feng, Karel Mundnich, Amrutha Nadarajan, Brandon M Booth, Emilio Ferrara, Kristina Lerman, Eric Hsieh, and Shrikanth Narayanan. Tiles-2019: A longitudinal physi ologic and behavioral data set of medical residents in an intensive care unit. Scientific Data, 9(1):536, 2022 [9]  Petter Jakobsen, Enrique Garcia Ceja, Lena Antonsen Stabell, Ketil Joachim Oedegaard, Jan Oystein Berle, Vajira Thambawita, Steven Alexan der Hicks, P˚ al Halvorsen, Ole Bernt Fasmer, and Michael Alexander Riegler. Psykose: A motor activity database of patients with schizophrenia. In 2020 IEEE 33rd International Symposium on Computer-Based Med ical Systems (CBMS), pages 303–308. IEEE, 2020 [10]  Lang He, Mingyue Niu, Prayag Tiwari, Pekka Marttinen, Rui Su, Jiewei Jiang, Chenguang Guo, Hongyu Wang, Songtao Ding, Zhongmin Wang, et al. Deep learning for depression recognition with audiovisual cues: A review. Information Fusion, 80:56–86, 2022.

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Publication Date

5/31/2026

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Double-Blind Peer Review

Review Rounds: 2

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