Predictive Model to Identify Burnout Syndrome in Medical Students
Article Sidebar
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Societal impact
Main Article Content
Abstract
Burnout syndrome represents a significant concern among medical students due to the high academic and emotional demands of their training. The use of machine learning techniques can facilitate early identification and support intervention processes. The objective of this study was to develop a model to predict burnout syndrome in students using machine learning techniques. Data from 81 medical students in Bogotá, Colombia were included, along with 140 simulated values incorporated into the total dataset analyzed. A preselection of relevant numerical features was conducted using the SelectKBest method. An optimized Random Forest model was implemented through hyperparameter tuning. Model performance was evaluated using precision, recall, and F1-score metrics. The developed model achieved 77% accuracy in classifying burnout syndrome levels among participants. In conclusion, the model demonstrated effectiveness in predicting burnout syndrome.
Article Details
References
Almutairi, H., Alsubaiei, A., Abduljawad, S., Alshatti, A., Fekih-Romdhane, F., Husni, M. y Jahrami, H. (2022). Prevalence of burnout in medical students: A systematic review and meta-analysis. International Journal of Social Psychiatry, 68(6), 1157-1170. https://doi.org/10.1177/00207640221106691
Baniadamdizaj, S. y Baniadamdizaj, S. (2023). Prediction of Iranian EFL teachers' burnout level using machine learning algorithms and Maslach Burnout Inventory. Iran Journal of Computer Science, 6(1), 1-12. https://doi.org/10.1007/s42044-022-00112-x
Bergstra, J. y Bengio, Y. (2012). Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1), 281-305. https://dl.acm.org/doi/
abs/10.5555/2188385.2188395
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://link.springer.com/article/10.1023/a:1010933404324
Caballero, C. C., Abello, R. y Palacio, J. (2007). Relación del burnout y el rendimiento académico con la satisfacción frente a los estudios en estudiantes universitarios. Avances en Psicología Latinoamericana, 25(2), 98-111. https://dialnet.unirioja.es/servlet/articulo?codigo=2542727
Chawla, N. V., Bowyer, K. W., Hall, L. O. y Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953
Dyrbye, L. N., Thomas, M. R. y Shanafelt, T. D. (2006). Systematic review of depression, anxiety, and other indicators of psychological distress among us and Canadian medical students. Academic Medicine, 81(4), 354-373. https://academic.oup.com/academicmedicine/article-abstract/81/4/354/8354952
Feher, G., Kapus, K., Tibold, A., Banko, Z., Berke, G., Gacs, B., Varadi, I., Nyulas, R. y Matuz, A. (2024). Mental Issues, internet addiction and quality of life predict burnout among hungarian teachers: A machine learning analysis. BMC Public Health, 24(1), 2322. https://doi.org/10.1186/s12889-024-19797-9
Fernández-Delgado, M., Cernadas, E., Barro, S. y Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 3133-3181. https://www.jmlr.org/papers/volume15/delgado14a/delgado14a.pdf?source=post_page---------------------------
Gibert, D., Mateu, C. y Planes, J. (2020). The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. Journal of Network and Computer Applications, 153, 102526. https://www.sciencedirect.com/science/article/pii/S1084804519303868
Gil-Monte, P. R. (2007). El síndrome de quemarse por el trabajo (burnout) como fenómeno transcultural. Informació Psicològica, (91-92), 4-11. https://www.informaciopsicologica.info/revista/article/view/259
Grządzielewska, M. (2021). Using machine learning in burnout prediction: A survey. Child and Adolescent Social Work Journal, 38(2), 175-180. https://link.springer.com/article/10.1007/s10560-020-00733-w
Guyon, I. y Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H. y Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220-239. https://doi.org/10.1016/j.eswa.2016.12.035
Hederich-Martínez, C. y Caballero-Domínguez, C. C. (2016). Validación del cuestionario Maslach Burnout Inventory-Student Survey (MBI-SS) en contexto académico colombiano. Revista CES Psicología, 9(1), 1-15. https://revistas.ces.edu.co/index.php/psicologia/article/view/3511
Hernández-Sampieri, R., Fernández, C. y Baptista, P. (2014). Metodología de la investigación (6ª ed.). McGraw-Hill.
IsHak, W., Nikravesh, R., Lederer, S., Perry, R., Ogunyemi, D. y Bernstein, C. (2013). Burnout in Medical Students: A Systematic Review. The Clinical Teacher, 10(4), 242-245. https://doi.org/10.1111/tct.12014
Kuadey, N. A., Ankora, C., Tahiru, F., Bensah, L., Agbesi, C. C. M. y Bolatimi, S. O. (2024). Using Machine Learning Algorithms to Examine the Impact of Technostress Creators on Student Learning Burnout and Perceived Academic Performance. International Journal of Information Technology, 16(4), 2467-2482. https://link.springer.com/article/10.1007/s41870-023-01655-3
Maslach, C. y Jackson, S. E. (1981). The Measurement of Experienced Burnout. Journal of Organizational Behavior, 2(2), 99-113. https://doi.org/10.1002/job.4030020205
Müller, A. C. y Guido, S. (2016). Introduction to machine learning with Python: A guide for data scientists. O'Reilly Media, Inc. https://books.google.com.co/books?hl=es&lr=&id=1-4lDQAAQBAJ&oi=fnd&pg=PP1&dq=M%C3%BCller,+A.+C.,+y+Guido,+S.+(2016).+Introduction+to+machine+learning+with+Python:+a+guide+for+data+scientists.+O%27Reilly+Media,+Inc.+&ots=29lTILNM0X&sig=Ka0KsbSjSKRp6O11eNWqGqh_q-k&redir_esc=y#v=onepage&q&f=false
Papini, S., Iturralde, E., Lu, Y., Greene, J. D., Barreda, F., Sterling, S. A. y Liu, V. X. (2023). Development and Validation of a machine learning model using electronic health records to predict trauma-and stressor-related psychiatric disorders after hospitalization with sepsis. Translational Psychiatry, 13(1), 400. https://www.nature.com/articles/s41398-023-02699-6
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. y Duchesnay, É. (2011). Scikit-Learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830. https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf?source=post_page
Pereira, M. G., Santos, M., Magalhães, R., Rodrigues, C., Araújo, O. y Durães, D. (2025). Burnout risk profiles in psychology students: An exploratory study with machine learning. Behavioral Sciences, 15(4), 505. https://doi.org/10.3390/bs15040505
Rosales-Ricardo, Y., Rizzo-Chunga, F., Mocha-Bonilla, J. y Ferreira, J. P. (2021). Prevalence of burnout syndrome in university students: A systematic review. Salud Mental, 44(2), 91-102. https://dialnet.unirioja.es/servlet/articulo?codigo=7903549
Saito, T., Suzuki, H. y Kishi, A. (2022). Predictive modeling of mental illness onset using wearable devices and medical examination data: Machine learning approach. Frontiers in Digital Health, 4, 861808. https://doi.org/10.3389/fdgth.2022.861808
Sufi, F. (2024). Generative Pre-Trained Transformer (GPT) in research: A systematic review on data augmentation. Information, 15(2), 99. https://doi.org/10.3390/info15020099
Tapio, R. P. (2025). Predicting burnout in college students: A machine learning approach using decision tree and psychometric data. Asian Journal of Probability and Statistics, 27(5), 50-60. https://doi.org/10.9734/ajpas/2025/v27i5754
Yi, L., Shuai, T., Zhou, J., Cheng, L., Jiménez-Herrera, M. F. y Tian, X. (2024). Development and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: A cross-sectional study utilizing latent profile analysis. BMC Medical Education, 24(1), 1495. https://doi.org/10.1186/s12909-024-06505-9