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

Milena Seibert Fernandes, Roberto Rodrigues Filho, Iwens Gervásio Sene Júnior, Stefan Sarkadi, Alison R. Panisson, Analúcia Schiaffino Morales

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 o