Predicting Employees under Stress for Pre-emptive Remediation using Machine Learning

Minhaj Begum, Bokka Greeshma Reddy and Nabiha

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022, vol. 8, issue 5, 334-337

Abstract: The modern world is filled with stress. Everyone is under pressure in a situation for one of two reasons. Aperson's pressure is affected by a variety of factors. Representatives in IT are more likely to be under pressure due to work pressure, overburdening, higher worker mastery, and so on. When a person is stressed, it can lead to a variety of mental health issues such as depression, anxiety, somatization, lack of concentration, and so on. It can sometimes be fatal. As a result, it is necessary to identify human stress at an early stage in order to provide appropriate solutions and alleviate stress. There has been a lot of research done on stress prediction. Many research papers use Machine Learning techniques to predict stress, and many papers use IOT-based sensors to extract the features needed for stress prediction. Many papers simply present the concept of stress prediction without any implementation. There are some research papers that include implementation. These implementation papers make use of ready-made tools such as the WEKA tool, the R tool, the Rapid Miner, or programming languages such as PYTHON or R. It is simple to predict stress using these ready tools and languages because they support ready libraries for stress prediction. Data science techniques are effective at processing training datasets and can predict human stress in less time and with higher accuracy.

Keywords: Data Science; Machine Learning; Stress; Information Technology Profession. (search for similar items in EconPapers)
Date: 2022
Note: Article URL: https://ijsrcseit.com/CSEIT2390273
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/CSEIT2390273 Article URL (text/html)
https://ijsrcseit.com/paper/CSEIT2390273.pdf Full text (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:jbh:ijsrcs:v8:y2022:i5:id:hcseit2390273

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().