Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Mental Health Detection: Detection And Classifying Of Anxiety Using Machine Learning

Authors
D. Tejaswi1, *, M. Harshini1, G. Shishira1, K. Manudeep1, K. Rahul1
1Department of Information Technology, Anil Neerukonda Institute of Technology and Sciences, Sangivalasa, Visakhapatnam, Andhra Pradesh, India
*Corresponding author. Email: tejaswi.it@anits.edu.in
Corresponding Author
D. Tejaswi
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_83How to use a DOI?
Keywords
Supervised Machine Learning; SVM - Support vector machine; Random forest
Abstract

Mental health has recently become a crucial area of concern across various fields, gaining significant attention due to the rising number of individuals suffering from mental disorders, especially anxiety. A large portion of these individuals includes university students, who face immense academic and social pressure. This research aims to assess the prevalence of anxiety among Indian university students, identify its causes and effects, and classify anxiety levels using machine learning techniques. The dataset was collected through a structured questionnaire based on a Likert scale and distributed among 127 engineering students. Statistical analyses, including Cronbach’s alpha (0.723) and Pearson’s correlation (0.823), were conducted to verify reliability and validity. Machine learning algorithms, including Naïve Bayes, Decision Tree, Random Forest, and SVM, were applied to classify anxiety levels. The accuracy achieved by these models was 71.05%, 71.05%, 78.9%, and 75.5%, respectively, with Random Forest yielding the highest performance.

Copyright
© 2025 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 International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_83How to use a DOI?
Copyright
© 2025 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  - D. Tejaswi
AU  - M. Harshini
AU  - G. Shishira
AU  - K. Manudeep
AU  - K. Rahul
PY  - 2025
DA  - 2025/11/04
TI  - Mental Health Detection: Detection And Classifying Of Anxiety Using Machine Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 992
EP  - 1006
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_83
DO  - 10.2991/978-94-6463-858-5_83
ID  - Tejaswi2025
ER  -