PREDICTING EMPLOYEES UNDER STRESS FOR PREEMPTIVE REMEDIATION USING MACHINE LEARNING ALGORITHM

Abstract

Employee stress has emerged as a critical concern in modern workplaces, impacting productivity, well-being, and organizational efficiency. Prolonged exposure to stress can lead to burnout, absenteeism, and declining performance, necessitating proactive measures for early identification and intervention. Predicting employees under stress through data-driven methodologies enables organizations to take pre-emptive remedial actions, fostering a healthier and more productive work environment. This study explores predictive models that leverage various indicators, including workload, work hours, sentiment analysis from communication channels, absenteeism records, and physiological markers (where applicable). Machine learning algorithms, natural language processing (NLP), and sentiment analysis techniques can help identify patterns that correlate with high stress levels. By integrating these predictive insights with human resource strategies, companies can provide timely interventions such as workload redistribution, mental health support, and stress management programs. The implementation of such predictive models requires ethical considerations, including employee consent, data privacy, and transparency in decision-making. Balancing technology with human-centric approaches ensures that interventions are supportive rather than intrusive. This research aims to highlight the significance of predictive analytics in stress management and its potential to enhance employee well-being while improving organizational outcomes.