Smart Mental Health Prediction for Employees Using Ensemble Learning

K. Manikanda Kumaran, S. Aljesirabanu, M. Anushree, B. Gowthami

2025

Abstract

Internal heartiness is defined as the lack of internal health problems. Instead, internal health is a state of well-being that allows employees to manage their work, accomplish their goals, learn and work efficiently, and positively influence their working environment. Employers' internal problems have a variety of detrimental effects on the association. Their ability to think, act, feel, socialize, and form relationships is also adversely affected. Therefore, it is imperative to promptly address the underlying health situation and implement appropriate treatments. This study's primary goal is to develop a machine literacy model that can predict employees' internal health conditions and the need for treatments. For this study, employees from non-technical, specialized businesses were used. Using techniques like Decision Tree (J48), Support Vector Machine (SVM), Random Forest, and Ensemble Learning, the gathered data samples are pre-processed and analysed. The delicacy position of the ensemble literacy that merged the algorithms below was 93.16. Ensemble literacy is the fashionable algorithm to read the position of the need for therapies for workers' internal health when compared to the J48.

Paper Citation

in Harvard Style

Kumaran K., Aljesirabanu S., Anushree M. and Gowthami B. (2025). Smart Mental Health Prediction for Employees Using Ensemble Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 781-787. DOI: 10.5220/0013905700004919

in Bibtex Style

@conference{icrdicct`2525,
author={K. Kumaran and S. Aljesirabanu and M. Anushree and B. Gowthami},
title={Smart Mental Health Prediction for Employees Using Ensemble Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={781-787},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013905700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Smart Mental Health Prediction for Employees Using Ensemble Learning
SN - 978-989-758-777-1
AU - Kumaran K.
AU - Aljesirabanu S.
AU - Anushree M.
AU - Gowthami B.
PY - 2025
SP - 781
EP - 787
DO - 10.5220/0013905700004919
PB - SciTePress