Abstract

Employee burnout due to high stress levels is one of the factors that impacts the productivity of employees as well as the quality of their output at work. It is therefore extremely important for HR personnel and management staff to predict and manage stress levels ahead of burnout. Traditional methods of identifying burnout often rely on retrospective assessments, limiting organizations’ ability to address it proactively. Machine Learning has been effective in performing predictive analytics on data and has been applied by researchers in similar use cases such as employee attrition prediction, applicant scoring using AI-enhanced ATS systems, and employee performance analysis. This research aims to apply descriptive analysis techniques in analyzing the causes of burnout rates in organizations and will also utilize select machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, Support Vector Classifier, Gradient Boosting, Extreme Gradient Boosting, Stacking Classifier and Neural Networks in building a predictive model that can predict employee burnouts in organizations and flag these predicted high stress levels with specified personnels for monitoring. This research uses primary data gotten from a wide variety of professionals, which is used to train a machine learning model that is deployed to a web endpoint where it can be accessed by everyone. This work explores the potential of data-driven approaches in supporting employee well-being and offers actionable insights for organizations aiming to mitigate burnout. Future work may focus on expanding data sources and refining predictive models to enhance scalability and applicability in diverse organizational contexts.

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