Correlation and Causal Analysis of Occupational Health Data in the Maritime Domain

Occupational health is an aspect often neglected in our society. Illness, injuries, and fatalities in the workplace have remarkable consequences from both societal as well as economical point of view. Fitness-for-work judgment is critical to deem whether workers are fit, unfit, or fit with restrictions for a specific job. Identifying the key factors and how these impact the workers’ life quality is paramount.In this study, we harness machine learning, pattern mining and causal learning to first identify key impacting factors and patterns associated with fitness-for-work decisions in the maritime domain, and then to extract and use a causal model to enable a what-if analysis on the identified factors aimed at predicting the outcome of potential improvement actions.The current results highlight the prominent role of factors like age, smoking, and audiometry values in the judgment in relation to the job type. We inferred the 10 most frequent patterns, validated against decision tree rules and whose significance is confirmed by the Relative Risk and Odds Ratio. These are then used to query an automatically extracted causal model to simulate potential improvement actions as well as to support root cause analysis. We used real-world data from two maritime companies according to their health protocols. The dataset is made publicly available, contributing to the relatively scarce empirical body of knowledge on occupational health.