Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)

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Comparison of Logistic Regression and Decision Tree Models for Mental Health Estimation of Employees

Authors
Muyun Li1, *
1College of Arts and Science, Vanderbilt University, Tennessee State, Nashville, 37235, USA
*Corresponding author. Email: muyun.li@vanderbilt.edu
Corresponding Author
Muyun Li
Available Online 27 November 2023.
DOI
10.2991/978-94-6463-300-9_3How to use a DOI?
Keywords
Mental Health; Logistic Regression; Decision Tree
Abstract

Mental health accompanies every human being inevitably and has great significance in helping people address life stress and realize their abilities. However, mental health is also a double-edged sword, which mental health issues can hinder people from carrying out daily activities normally and keeping in a good mood. Not only should the general public be aware of the importance of their mental health, but also those industries that rely on human resources should pay special attention to their employees’ mental health in order for the normal operation of the essential tasks. This paper aims at constructing feasible models helpful for normal people to predict their own mental state and organizations to predict their employees’ mental health state. To predict the mental health state, this paper examines two models of logistic regression and decision tree classifiers. The results indicate that logistic regression is relatively stable but not perfect in accuracy, positive predictive value, and true positive rate while decision tree classifiers are excellent at positive predictive value but poor at true positive rate.

Copyright
© 2023 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 the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
Series
Advances in Computer Science Research
Publication Date
27 November 2023
ISBN
978-94-6463-300-9
ISSN
2352-538X
DOI
10.2991/978-94-6463-300-9_3How to use a DOI?
Copyright
© 2023 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  - Muyun Li
PY  - 2023
DA  - 2023/11/27
TI  - Comparison of Logistic Regression and Decision Tree Models for Mental Health Estimation of Employees
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 16
EP  - 22
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_3
DO  - 10.2991/978-94-6463-300-9_3
ID  - Li2023
ER  -