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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.25509 |
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| _version_ | 1866915585444544512 |
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| author | Teodosio, Bruno W. G. Lira, Mário J. O. T. Araújo, Pedro H. M. Farias, Lucas R. C. |
| author_facet | Teodosio, Bruno W. G. Lira, Mário J. O. T. Araújo, Pedro H. M. Farias, Lucas R. C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use Teodosio, Bruno W. G. Lira, Mário J. O. T. Araújo, Pedro H. M. Farias, Lucas R. C. Machine Learning 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. |
| title | Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.25509 |