Forecasting of Stressed Employees by Using Machine Learning Algorithms for Effective Pre-emptive Remediation

The workplace environment plays a crucial role in the overall well-being of employees, and stress is a prevalent concern that can significantly impact productivity and employee satisfaction. This research addresses the challenge of proactively identifying employees under stress and implementing pre-emptive remediation strategies through the application of machine learning techniques. The proposed system leverages historical and real-time data, including work-related metrics, communication patterns, and potentially physiological indicators, to predict stress levels among employees. The machine learning model employed in this study is trained on a diverse dataset, allowing it to learn patterns associated with stressed and non-stressed states. Through continuous monitoring, the system generates alerts when an employee is predicted to be under significant stress, prompting timely intervention by Human Resources professionals. Remediation strategies may include targeted support, workload adjustments, or other measures aimed at alleviating stress and fostering a healthier work environment. The implementation of this predictive system aims to contribute to the well-being of employees, enhance organizational efficiency, and create a workplace culture that prioritizes mental health. By proactively addressing stressors, organizations can foster a more resilient and productive workforce while demonstrating a commitment to the holistic health and satisfaction of their employees.