A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa

Aug 5, 2026

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Healthcare demand is increasing worldwide, while nurses in South Africa face limited human and material resources, fostering burnout and emotional exhaustion. The study examines specific factors linked to these outcomes and assesses whether they can be predicted using demographic data alone. Supervised machine learning classifiers are trained on 1,165 survey responses and evaluated with accuracy, AUC, and confusion matrices. Results show full-survey models outperform demographic-only models, and fatigue emerges as the strongest predictor.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusions

Keywords

Background

Determining factors and prior approaches

Van Zyl‑Cillié et al. BMC Health Services Research (2024) 24:1665 [https://doi.org/10.1](https://doi.org/10.1) 186/s12913‑024‑12184‑5 BMC Health Services Research RESEARCH Open Access A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa Maria Magdalena Van Zyl‑Cillié1,2* , Jacoba H. Bührmann 1 , Alwiena J. Blignaut3 , Derya Demirtas2 and Siedine K. Coetzee3 Abstract Background The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pres‑ sure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nursesand informing policymakers. Currently, little is known about the unique factors associated with burnout and emo‑ tional exhaustion among nurses in South Africa. Furthermore, whether these factors can be predicted using demo‑ graphic data alone is unclear. Machine learning has recently been proven to solve complex problems and accurately predict outcomes in medical settings. In this study, supervised machine learning models were developed to identify the factors that most strongly predict nurses reporting feelings of burnout and experiencing emotional exhaustion. Methods The PyCaret 3.3 package was used to develop classification machine learning models on 1165 collected survey responses from nurses across South Africa in medical‑surgical units. The models were evaluated on their accuracy score, Area Under the Curve (AUC) score and confusion matrix performance. Additionally, the accuracy score of models using demographic data alone was compared to the full survey data models. The features with the high‑ est predictive power were extracted from both the full survey data and demographic data models for comparison. Descriptive statistical analysis was used to analyse survey data according to the highest predictive factors. Results The gradient booster classifier (GBC) model had the highest accuracy score for predicting both self‑reported feelings of burnout (75 . 8%) and emotional exhaustion (76 . 8%) from full survey data. For demographic data alone, the accuracy score was 60.4% and 68. 5%, respectively, for predicting self‑reported feelings of burnout and emotional exhaustion. Fatigue was the factor with the highest predictive power for self‑reported feelings of burnout and emo‑ tional exhaustion. Nursing staff’s confidence in management was the second highest predictor for feelings of burnout whereas management who listens to employees was the second highest predictor for emotional exhaustion. Conclusions Supervised machine learning models can accurately predict self‑reported feelings of burnout or emo‑ tional exhaustion among nurses in South Africa from full survey data but not from demographic data alone. The *Correspondence: Maria Magdalena Van Zyl‑Cillié [maria.vanzyl@nwu.ac.za](maria.vanzyl@nwu.ac.za) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licen

FAQs

What problem does the study address for South African nursing staff?

It addresses burnout and emotional exhaustion by identifying risk factors and testing whether they can be predicted reliably.

How were the machine learning models developed and evaluated?

Supervised models were trained using PyCaret 3.3 on 1,165 survey responses, then evaluated with accuracy, AUC, and confusion matrices.

Which factors showed the highest predictive power?

Fatigue was the top predictor for both burnout and emotional exhaustion, followed by confidence in management for burnout and management who listens to employees for emotional exhaustion.

Aug 5, 2026

Upload by Quinn