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Mental Stress Detection Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Computer Science
Abstract
This paper pivots on detecting mental stress levels among employees and students using a machine learning model called the Random Forest Classifier. A dataset from Kaggle, based on employees and students and emotional responses to various questions, was used to calculate stress scores. The model is focused to achieve 100% training accuracy and 95% test accuracy, proving its reliability. A web application was developed using Flask, where user answer the questions, and the system predicts their stress levels. This non-invasive tool can help identify high-stress individuals early, enabling timely support and promoting mental health. This project aims to foster well-being in the society through technology-driven solutions. By leveraging machine learning, it promotes mental health support within society, fostering a health.
Keywords
Machine Learning, Mental Stress Detection, Random Forest Classifier.
References
[1] Adnan, Nadia, et al. "University students stress level and brainwave balancing index: Comparison between early and end of study semester." Research and Development (SCOReD), 2012 IEEE Student Conference on. IEEE, 2012.
[2] Subhani, Ahmad Rauf, Wajid Mumtaz, Mohamed Naufal Bin Mohamed Saad, Nidal Kamel, and Aamir Saeed Malik. "Machine learning framework for the detection of mental stress at multiple levels." IEEE Access 5 (2017): 13545-13556.
[3] Xu, Q., Nwe, T.L., Guan, C.. Cluster-based analysis for personalized stress evaluation using physiological signals. IEEE Journal of biomedical and health informatics 2015; 19(1):275–281.
[4] Ghaderi, A., Frounchi, J., Farnam, A.. Machine learning-based signal processing using physiological signals for stress detection. In: 2015 22nd Iranian Conference on Biomedical Engineering (ICBME). 2015, p. 93–98.
[5] Dr. Robert Schachter. “Mindfulness for Stress Management: 50 Ways to Improve Your Mood and Cultivate Calmness” August 13, 2019.
[6] Cary L. Cooper, James Campbell Quick. “The Handbook of Stress and Health: A Guide to Research and Practice” 18 February 2017.
How to cite this paper
@article{1707576,
author = {Dinesh A, Bhalanath Mohanta, Byrava M, Humaun Forhat},
title = {Mental Stress Detection Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {1042-1045},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707576.pdf},
abstract = {This paper pivots on detecting mental stress levels among employees and students using a machine learning model called the Random Forest Classifier. A dataset from Kaggle, based on employees and students and emotional responses to various questions, was used to calculate stress scores. The model is focused to achieve 100% training accuracy and 95% test accuracy, proving its reliability. A web application was developed using Flask, where user answer the questions, and the system predicts their stress levels. This non-invasive tool can help identify high-stress individuals early, enabling timely support and promoting mental health. This project aims to foster well-being in the society through technology-driven solutions. By leveraging machine learning, it promotes mental health support within society, fostering a health.},
keywords = {Machine Learning, Mental Stress Detection, Random Forest Classifier.},
month = {March},
}