An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals

Milena Fernandes, Roberto Filho, Iwens Sene-Junior, Stefan Sarkadi, Alison R. Panisson, Analúcia Morales

2024

Abstract

In the last few years, several scientific studies have shown that occupational stress has a significant impact on workers, particularly those in the healthcare sector. This stress is caused by an imbalance between work conditions, the worker’s ability to perform their tasks, and the social support they receive from colleagues and management professionals. Researchers have explored occupational stress as part of a broader study on affective systems in healthcare, investigating the use of biomarkers and machine learning approaches to identify early conditions and avoid Burnout Syndrome. In this paper, a set of machine learning (ML) algorithms was evaluated using statistical data on biomarkers from the AffectiveRoad database to determine whether the use of explanations can help identify stress more objectively. This research integrates explainability and machine learning to aid in the identification of various levels of stress, which has not been previously evaluated for the domain of occupational stress. The Random Forest is the best-performing model for this assignment, followed by k-Nearest Neighbors and Neural Network. Later, explainers were applied to the Random Forest, highlighting feature importance, partial dependencies between characteristics, and a summary of the impact of features on outputs based on their values.

Paper Citation

in Harvard Style

Fernandes M., Filho R., Sene-Junior I., Sarkadi S., Panisson A. and Morales A. (2024). An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: EAA; ISBN 978-989-758-680-4, SciTePress, pages 499-506. DOI: 10.5220/0012591300003636

in Bibtex Style

@conference{eaa24,
author={Milena Fernandes and Roberto Filho and Iwens Sene-Junior and Stefan Sarkadi and Alison R. Panisson and Analúcia Morales},
title={An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: EAA},
year={2024},
pages={499-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012591300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: EAA
TI - An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals
SN - 978-989-758-680-4
AU - Fernandes M.
AU - Filho R.
AU - Sene-Junior I.
AU - Sarkadi S.
AU - Panisson A.
AU - Morales A.
PY - 2024
SP - 499
EP - 506
DO - 10.5220/0012591300003636
PB - SciTePress