Predicting Employees Under Stress for Pre-Emptive Remediation Using Machine Learning Algorithm

Abstract

The modern world is filled with stress. Everyone is under pressure in a situation for one of two reasons. A person'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.