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

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Data Mining and Machine Learning based Student Mental Health Identification across Telangana Regions in India

Authors
G. Merlin Linda1, *, Sudheer Reddy Bandi2, B. Yugandhara Chary3, N. Kamala Vikasini3, D. Kavya3, D. Kalyani3, B. Roshan3
1Associate Professor, Vidya Jyothi Institute of Technology, Hyderabad, Telangana, India
2Associate Professor, J. B. Institute of Engineering and Technology, Hyderabad, Telangana, India
3Swarna Bharathi Institute of Science and Technology, Khammam, Telangana, India
*Corresponding author. Email: merlingcse@vjit.ac.in
Corresponding Author
G. Merlin Linda
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_43How to use a DOI?
Keywords
Mental Health; Students; Machine Learning; Career; Health; Relationships; Treatment
Abstract

The proposed work uses a multifaceted approach to address mental health concerns among students, incorporating various machine learning algorithms to improve accuracy and efficacy. Mental health plays a vital role in the overall success of a student, both in school and in life. When students take care of their mental well-being, they tend to excel academically, build stronger relationships, and lead more fulfilling lives. Prioritizing mental health can truly make a difference in how students experience their education and personal growth. Using the power of Logistic Regression, Support Vector Classifier (SVC), Stacking Classifier, and the proposed methodology, we offer a sophisticated platform to assess mental health levels and provide personalized recommendations for treatment and relaxation. The system identifies early signs of mental health problems by analyzing various data sources and user preferences and delivers tailored interventions. Through a combination of advanced analytics and user-centric design, the current work aims to promote proactive mental health management and foster a supportive academic environment conducive to well-being. In addition, the results enhance the importance of machine learning algorithms in the field of health care, especially the consideration of categories such as relationships, health, career, and the results related to it.

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_43How 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  - G. Merlin Linda
AU  - Sudheer Reddy Bandi
AU  - B. Yugandhara Chary
AU  - N. Kamala Vikasini
AU  - D. Kavya
AU  - D. Kalyani
AU  - B. Roshan
PY  - 2025
DA  - 2025/11/04
TI  - Data Mining and Machine Learning based Student Mental Health Identification across Telangana Regions in India
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 493
EP  - 509
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_43
DO  - 10.2991/978-94-6463-858-5_43
ID  - Linda2025
ER  -