Prediction of Stress Level on Indian Working Professionals Using Machine Learning

Kavita Pabreja, Anubhuti Singh, Rishabh Singh, Rishita Agnihotri, Shriam Kaushik and Tanvi Malhotra
Additional contact information
Kavita Pabreja: Maharaja Surajmal Institute, GGSIP University, India
Anubhuti Singh: Deloitte, India
Rishabh Singh: Deloitte, India
Rishita Agnihotri: Deloitte, India
Shriam Kaushik: Prague University of Economics and Business, Czech Republic
Tanvi Malhotra: Deloitte, India

International Journal of Human Capital and Information Technology Professionals (IJHCITP), 2022, vol. 13, issue 1, 1-26

Abstract: Stress levels amongst the Indian employees have increased due to a variety of factors and are a matter of great concern for the organizations. This study is based on Indian working professionals and real data has been collected by using non-probability convenience sampling. A questionnaire was drafted based on eighteen factors affecting the mental health of professionals. This study addresses two dimensions, first is to identify the important influential features that trigger stress in the lives of working professionals, and the second is to predict the stress levels. Various supervised machine learning algorithms have been experimented with and of all these algorithms, the Support Vector Machine Regressor model showed the best performance. The main contribution of the paper lies in the identification and ranking of ten important stress triggering features, that can guide organizations to develop policies to take care of their employees. The other deliverable is the development of a GUI-based stress prediction software based on Machine learning techniques.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... .4018/IJHCITP.297077 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jhcitp:v:13:y:2022:i:1:p:1-26

Access Statistics for this article

International Journal of Human Capital and Information Technology Professionals (IJHCITP) is currently edited by Sanjay Misra

More articles in International Journal of Human Capital and Information Technology Professionals (IJHCITP) from IGI Global
Bibliographic data for series maintained by Journal Editor ().