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

Aug 3, 2026

Occupational stress significantly affects workers in healthcare, arising from mismatches between work conditions, employees’ capability to perform tasks, and the social support available from colleagues and management. The study evaluates multiple machine learning algorithms to determine whether explanation methods improve objective stress identification using statistical biomarker data from the AffectiveRoad database. Explainability is integrated to support detection across stress levels, with Random Forest performing best, followed by k-Nearest Neighbors and Neural Network.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Occupational stress in healthcare

Questionnaire-based identification and its bias

Sensor-based biomarkers and machine learning

King’s Research Portal Document Version Peer reviewed version Link to publication record in King's Research Portal Citation for published version (APA): Seibert Fernandes, M. , Rodrigues Filho, R. , Sene-Junior, I. , Sarkadi, S. , Panisson, A. R. , & Schiaffino Morales, A. (Accepted/In press) . An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals. In 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) SciTePress. Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rights Copyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights. •Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research. •You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact [librarypure@kcl.ac.uk](librarypure@kcl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim. Download date: 08. May. 2024 An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals Milena Seibert Fernandes 1 , Roberto Rodrigues Filho 1 , Iwens Sene-Junior2 , Stefan Sarkadi3 , Alison R. Panisson 1 and Anal´ucia Schiaffino Morales 1 1 Department of Computing, Federal University of Santa Catarina, Santa Catarina, Brazil 2 Institute of Informatics, Federal University of Goi a´s, Goiaˆnia, Brazil 3 Department of Informatics, King’s College London, London, U.K. [iwens@ufg.br](iwens@ufg.br), [stefan.sarkadi@kcl.ac.uk](stefan.sarkadi@kcl.ac.uk),{analucia.morales, [alison.panisson](alison.panisson}@ufsc.br)[}](alison.panisson}@ufsc.br)[@ufsc.br](alison.panisson}@ufsc.br) Keywords: Interpretable ML, Stress, AI for Healthcare. 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

FAQs

What causes occupational stress in healthcare professionals according to the document?

Occupational stress is attributed to an imbalance between work conditions, the worker’s ability to perform tasks, and the social support received from colleagues and management.

How does the paper identify occupational stress?

It evaluates machine learning algorithms using statistical biomarker data from the AffectiveRoad database and applies explainability methods to interpret model outputs.

Which model performed best and how were explanations used?

Random Forest achieved the best performance. Explanations were applied to highlight feature importance, partial dependencies between characteristics, and how feature values impact outputs.

Aug 3, 2026

Uploaded by Emma Wilson