A Study of Mental Health Issues and Their Prediction Among Young Students Using Machine Learning Models

DOI

10.2991/978-94-6239-789-7_9How to use a DOI?

Keywords

Mental Disorders; Machine Learning; Prediction Model; Random Forest; SVM; Geriatric Health; Data Mining; Artificial Intelligence

Abstract

This research investigates the current scenario of mental disorders among aged individuals and proposes a machine learning–based prediction model to identify and classify major geriatric mental health issues. Accuracy metrics, confusion matrices, ROC analysis, and feature importance ranking are used to evaluate prediction performance using multiple supervised learning algorithms, such as Random Forest, SVM, Logistic Regression, and Naive Bayes. The results indicate that ensemble learning significantly improves diagnostic accuracy, enabling early detection and supporting clinical decision-making. [9, 27]

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.

Download article (PDF)

TY  - CONF
AU  - Rohit Rana
AU  - Rajkumar
PY  - 2026
DA  - 2026/10/08
TI  - A Study of Mental Health Issues and Their Prediction Among Young Students Using Machine Learning Models
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 112
EP  - 127
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_9
DO  - 10.2991/978-94-6239-789-7_9
ID  - Rana2026
ER  -