Enhancing Employee Well-being with Machine Learning: Predictive Models for Health and Wellness Programs

Abstract

This research explores the use of machine learning (ML) to enhance employee well-being through predictive models designed to optimize health and wellness programs. Using the Random Forest algorithm, employee health data—such as activity levels, sleep patterns, and stress metrics—are analyzed to identify individuals at risk of health issues. By leveraging Python's Scikit-learn library, predictive insights are derived to recommend personalized wellness interventions. Results demonstrate that the model achieves high accuracy in predicting outcomes like burnout and health deterioration, enabling proactive support. The research highlights the potential of ML-driven wellness solutions in fostering healthier, more productive workplaces.