Machine Learning Based Stress Detection for IT Professionals Using Random Forest algorithm - A. Udaya Sree,; S. Noortaj, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
In today’s fast-paced tech world, managing stress is becoming more crucial, especially for those in IT. The work culture in this industry often means long hours, tight deadlines, and high expectations, which can ramp up stress levels. When stress goes unchecked, it not only takes a toll on professionals' health and well-being but also impacts their productivity and job satisfaction. This study aims to predict stress levels among IT professionals using machine learning techniques, helping to promote proactive stress management. We look at various indicators of work-related stress, such as Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These factors give us a well-rounded view of both the physical and work-related elements that contribute to stress. By applying machine learning in this area, we’re taking an innovative approach to a growing concern. With the power of data analytics, this model seeks to offer practical insights for both individuals and organizations. Individuals can use these predictions for self-monitoring and early intervention, while organizations can pinpoint high-stress roles or environments, allowing them to allocate resources or interventions more effectively.
Keywords: Random forest; adaboost; extra tree