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Prior studies connect stress identification to affective-system research, using biomarkers and machine learning to detect early conditions and help prevent burnout. This work evaluates ML models on biomarker statistics from the AffectiveRoad database, testing whether explanations improve objective stress identification and enables interpretable insights across stress levels.","King’s Research Portal  \nDocument Version  \nPeer reviewed version  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nSeibert Fernandes, M. , Rodrigues Filho, R. , Sene-Junior, I. , Sarkadi, S. , Panisson, A. R. , & Schiaffino Morales, A. (in press) . An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals. 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Dec. 2025  \nAn Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals  \nMilena Seibert Fernandes 1 , Roberto Rodrigues Filho 1 , Iwens Sene-Junior2 , Stefan Sarkadi3 ,  \nAlison R. Panisson 1 and Anal´ucia Schiaffino Morales 1  \n1 Department of Computing, Federal University of Santa Catarina, Santa Catarina, Brazil  \n2 Institute of Informatics, Federal University of Goi a´s, Goiaˆnia, Brazil  \n3 Department of Informatics, King’s College London, London, U.K.  \n[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)  \nKeywords: Interpretable ML, Stress, AI for Healthcare.  \nAbstract: 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 ","cbCaipdwNwXN0Uc6","https://ap.wps.com/l/cbCaipdwNwXN0Uc6","pdf",350654,1,9,"English","en",105,"# Abstract\n# 1 INTRODUCTION\n## Occupational stress impact in healthcare\n## Biomarkers and machine learning for non-invasive identification","[{\"question\":\"What causes occupational stress in healthcare professionals according to the study?\",\"answer\":\"The study links occupational stress to an imbalance between work conditions, the worker’s ability to perform tasks, and the social support received from colleagues and management professionals.\"},{\"question\":\"What data and methods are used to identify stress levels?\",\"answer\":\"The research evaluates machine learning algorithms using statistical data on biomarkers from the AffectiveRoad database.\"},{\"question\":\"How do explanations contribute to stress identification results?\",\"answer\":\"Explainability is applied to interpret the best-performing model, highlighting feature importance and how feature values influence outputs through partial dependencies and summary impact analyses.\"}]","An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals | 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