Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

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A Machine Learning Approach to Predicting Depression in University Students in Bangladesh: Enhancing Mental Health Assessment

Authors
MD. Alamin1, *, Md Tasnin Tanvir2, Zahinul Haque Chowdhury3, Md Abdullah1, Tahsan Mahmood Tariq4, Ahnaf Tahmid Jamee5, Md Pervez Hossain6, Abdul Kader7, MD. Tahmeed Kowsher Hameem1
1Daffodil International University, Dhaka, Bangladesh
2Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh
3Purdue University Fort Wayne, Fort Wayne, Indiana, United States
4Bangladesh University of Health Sciences, Dhaka, Bangladesh
5Wentworth Institute of Higher Education, Surry Hills, Australia
6Northern University Bangladesh, Dhaka, Bangladesh
7Lamar University, Texas, United States
*Corresponding author. Email: amin15-6197@s.diu.edu.bd
Corresponding Author
MD. Alamin
Available Online 8 June 2026.
DOI
10.2991/978-94-6239-664-7_23How to use a DOI?
Keywords
Depression prediction; Mental health; Predictive analytics; Machine learning; University students
Abstract

This study investigates the application of machine learning (ML) algorithms in predicting depression severity among university students in Bangladesh using the Patient Health Questionnaire-9 (PHQ-9) dataset. A total of 577 students participated in the study, with data collected via an online survey that included the PHQ-9 alongside other socio-demographic information. The research evaluates the performance of six machine learning classifiers: Logistic Regression, Random Forest, Gradient Boosting, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), and Voting Classifier. The findings reveal that the Voting Classifier outperformed all other models, achieving an accuracy of 98.70%, followed by Gradient Boosting and Random Forest. The results highlight the potential of ML in early detection and intervention for mental health issues, particularly depression, within the context of Bangladesh’s university student population. The study underscores the importance of addressing ethical considerations, such as privacy and informed consent, when utilizing AI in sensitive health contexts. This research contributes to the growing body of work advocating for the integration of predictive analytics in mental health diagnostics, offering a promising pathway for future applications in mental wellness strategies.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
8 June 2026
ISBN
978-94-6239-664-7
ISSN
1951-6851
DOI
10.2991/978-94-6239-664-7_23How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - MD. Alamin
AU  - Md Tasnin Tanvir
AU  - Zahinul Haque Chowdhury
AU  - Md Abdullah
AU  - Tahsan Mahmood Tariq
AU  - Ahnaf Tahmid Jamee
AU  - Md Pervez Hossain
AU  - Abdul Kader
AU  - MD. Tahmeed Kowsher Hameem
PY  - 2026
DA  - 2026/06/08
TI  - A Machine Learning Approach to Predicting Depression in University Students in Bangladesh: Enhancing Mental Health Assessment
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 331
EP  - 340
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_23
DO  - 10.2991/978-94-6239-664-7_23
ID  - Alamin2026
ER  -