JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Identifying Stress Factors in IT workers using Machine Learning.
Sabreen M, Dr. Raghavedra S P
In the modern world, stress is a growingly important issue that is endangering people's health. The aim of this study is to detect overworked IT staff by using machine learning. An increased risk of depression, suicide, heart attacks, and strokes is associated with stress. There are several physical, emotional, and mental reactions that the body naturally has to change. There are three algorithms utilized for classification: decision tree, random forest, and logistic regression. The Random Forest algorithm provides the highest accuracy when compared to the other three. Older stress monitoring systems lacked live detection and individual counselling. Our technology is an upgraded version of those systems. In this, stress detection techniques that do not need individual counselling or realtime monitoring are updated. Effective stress management strategies are provided by gathering information on employees mental stress levels through a survey. This paper will examine stress management strategies and how to establish a work atmosphere that promotes spontaneity and wellness in order to maximize employee performance
Classification, Machine learning, Stress, Strokes, Heart attack