Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use
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 -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.
Cite
@article{arxiv.2510.25509,
title = {Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use},
author = {Bruno W. G. Teodosio and Mário J. O. T. Lira and Pedro H. M. Araújo and Lucas R. C. Farias},
journal= {arXiv preprint arXiv:2510.25509},
year = {2025}
}
Comments
12 pages, including figures and references. Streamlit app available at: https://employee-burnout-svm.streamlit.app/