Development of an AI-Based Student Depression Detection System Using the Multinomial Naïve Bayes Algorithm

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John Joshua E. Mendoza
College of Informatics, Philippine Christian University, 1648 Taft Ave, Malate, Manila, Philippines
Roman B. Villones
College of Informatics, Philippine Christian University, 1648 Taft Ave, Malate, Manila, Philippines
Rhayz Steven Kyle P. Bautista
College of Informatics, Philippine Christian University, 1648 Taft Ave, Malate, Manila, Philippines
Alfred Brian C. Bautista
College of Informatics, Philippine Christian University, 1648 Taft Ave, Malate, Manila, Philippines
Published: May 28, 2026

Depression among students is a major mental health concern that interferes with both their emotional and academic. Depressed students go unnoticed since they hide their symptoms or confuse them with normal stress. Depression often affects focus and attention, making it hard for students to follow lessons, complete assignments, or retain information. Having a detection system in place enables early intervention that can help educators to detect warning signs early. This research used Agile Software Development Methodology in designing and developing a student depression detection system. This methodology enables continues feedback and enhancement to ensure the system is effective in meeting user needs. The process of development is segmented into short cycles referred to as sprints, with each sprint dedicated to developing particular features. For the evaluation, we selected eight (8) participants and five (5) IT experts for their technical expertise and three (3) mental health professionals for their relevance to the system’s purpose. ISO/IEC 25010 is a global standard that outlines software quality characteristics and evaluation criteria to ensure the systems' functionality, reliability, usability, efficiency, maintainability, and portability. With an overall weighted mean of 3.61 for the IT experts rated the system as highly acceptable. Similarly, the Mental Health Professionals evaluated the Student Depression Detection System with a highly acceptable rating of 3.71, reinforcing its support for student well-being. Researchers identified that the student depression detection system is a useful resource for the early identification of depression in students. It is advised to adopt the student depression detection system in colleges and schools to aid in the identification of vulnerable students.
Development of an AI-Based Student Depression Detection System Using the Multinomial Naïve Bayes Algorithm. (2026). International Journal of Latest Technology in Engineering Management & Applied Science, 15(5), 661-671. https://doi.org/10.51583/IJLTEMAS.2026.150500054

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References

Andrews, B., Hejdenberg, J., & Wilding, J. (2006). Student anxiety and depression: comparison of questionnaire and interview assessments. Journal of affective disorders, 95(1-3), 29-34.

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

Becker, E. S., Margraf, J., Türke, V., Soeder, U., Neumer, S., & Lotz, G. (2020). Predicting academic achievement and grade retention with depression, anxiety, and stress scales. International Journal of Environmental Research and Public Health, 17(11), 3901. https://doi.org/10.3390/ijerph17113901

Cruz, F. T., Flores, E. E. C., & Quispe, S. J. C. (2023). Prediction of depression status in college students using a Naive Bayes classifier based machine learning model. arXiv preprint arXiv:2307.14371.

Deshpande, M., & Rao, V. (2017, December). Depression detection using emotion artificial intelligence. In 2017 international conference on intelligent sustainable systems (iciss) (pp. 858-862). IEEE.

Estdale, J., & Georgiadou, E. (2018, August). Applying the ISO/IEC 25010 quality models to software product. In European Conference on Software Process Improvement (pp. 492-503). Cham: Springer International Publishing.

Ibrahim, A. K., Kelly, S. J., Adams, C. E., & Glazebrook, C. (2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391–400. https://doi.org/10.1016/j.jpsychires.2012.11.015

Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health information science and systems, 6, 1-12.

Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British journal of applied science & technology, 7(4), 396.

Liu, S., Yin, Y., Qiu, J., Zhang, Y., & Zhu, X. (2020). Detecting depression in college students through text-based machine learning. Journal of Affective Disorders, 273, 770–776. https://doi.org/10.1016/j.jad.2020.05.120

Moir, F., Yielder, J., Sanson, J., & Chen, Y. (2018). Depression in medical students: current insights. Advances in medical education and practice, 323-333.

Mulyani, S., & Novita, R. (2022). Implementation of the Naive Bayes Classifier Algorithm for Classification of Community Sentiment About Depression on Youtube. Jurnal Teknik Informatika (Jutif), 3(5), 1355-1361.

Nikolaieva, A. (2024) Software Development Process: Definition, Methodologies And Key Steps. Retrieved from: https://www.uptech.team/blog/product-development-life-cycle

Rao, A., Krishnan, S., & Vahia, V. N. (2022). Artificial intelligence in mental health: Applications and ethical considerations. Journal of Mental Health and Technology, 5(2), 89–103. https://doi.org/10.1007/s10916-022-01836-7

Samanvitha, S., Bindiya, A. R., Sudhanva, S., & Mahanand, B. S. (2021, December). Naïve Bayes Classifier for depression detection using text data. In 2021 5th international conference on electrical, electronics, communication, computer technologies and optimization techniques (ICEECCOT) (pp. 418-421). IEEE.

Shen, T., Wang, J., & Huang, C. (2021). AI-based detection of student mental health issues using facial recognition and voice analysis. Computers in Human Behavior, 120, 106743. https://doi.org/10.1016/j.chb.2021.106743

Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., & Campbell, A. T. (2021). StudentLife: Using smartphones to assess mental health and academic performance of college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1), 1–26. https://doi.org/10.1145/3448129

World Health Organization. (2023). Depression. https://www.who.int/news-room/fact-sheets/detail/depression

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Development of an AI-Based Student Depression Detection System Using the Multinomial Naïve Bayes Algorithm. (2026). International Journal of Latest Technology in Engineering Management & Applied Science, 15(5), 661-671. https://doi.org/10.51583/IJLTEMAS.2026.150500054