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arXiv · 2510.25509

Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use

Bruno W. G. Teodosio·Mário J. O. T. Lira·Pedro H. M. Araújo·Lucas R. C. Farias
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Abstract

Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algorithms were evaluated: nearest neighbors (KNN), random forest, and support vector machine (SVM), with model performance evaluated through 30-fold cross-validation using the determination coefficient (R2). Among the models tested, SVM achieved the highest predictive performance (R2 = 0.84) and was statistically superior to KNN and Random Forest based on paired $t$-tests. To ensure practical applicability, an interactive interface was developed using Streamlit, allowing non-technical users to input data and receive burnout risk predictions. The results highlight the potential of machine learning to support early detection of burnout and promote data-driven mental health strategies in organizational settings.

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Bruno W. G. Teodosio, Mário J. O. T. Lira, Pedro H. M. Araújo, Lucas R. C. Farias. 2025-10-29. Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use. https://arxiv.org/abs/2510.25509

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