Early Detection of Mental Health Disorders Among Private University Students in Bangladesh Using Machine Learning-Based Behavioral Data Analysis

DOI

10.2991/978-94-6239-664-7_29How to use a DOI?

Keywords

Machine Learning; Mental Health Detection; Private University Students; Early Detection; Behavioral Data Analysis; Hyperparameter Tuning

Abstract

Mental health problems, such as depression, anxiety and stress are becoming increasingly common among university students and private university students in Bangladesh become more vulnerable to these disorders due to many other pressures (i.e., high tuition fees, rigid academic rules and social norms). Stigma and poor recognition of mental health disease also serve to hamper early detection and management. Conventional mental health evaluations are subjective and incapable to achieve the early detection of such diseases. In this work, we investigate the feasibility of detecting early signs of mental health disorders among students in private universities, using ML approaches on non-invasive student behavioral data including sleep, study and digital activity. A number of models, such as SAINT, Node, TabNet and CatBoost were tested before and after hyperparameter tuning. Here the 4 best sequential model, CatBoost and SAINT got good scores, however CatBoost had a test accuracy of 99.79% after we tuned it. Concurrently, an ensemble model containing CatBoost, SAINT and Node also obtained a high test accuracy of 98.23%. Results also indicate that ML-based analysis of behavioral data can provide a data-driven way of early detection, intervention and mental health improvement for private university students.

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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TY  - CONF
AU  - Shovan Samanta Turzo
AU  - K. M. Arafat Islam
AU  - Md. Sazzadur Ahamed
AU  - Md. Fokhray Hossain
PY  - 2026
DA  - 2026/06/08
TI  - Early Detection of Mental Health Disorders Among Private University Students in Bangladesh Using Machine Learning-Based Behavioral Data Analysis
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 409
EP  - 426
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_29
DO  - 10.2991/978-94-6239-664-7_29
ID  - Turzo2026
ER  -