Objective: To investigate the feasibility of predicting the risk of underground coal mine operations using data from the National Institute for Occupational Safety and Health (NIOSH).

Methods: A total of 22,068 data entries from 3,982 unique underground coal mines from 1990 to 2020 were extracted from the NIOSH mine employment database. We defined the risk index of a mine as the ratio between the number of injuries and the size of the mine. Several machine learning models were used to predict the risk of a mine based on its employment demographics (i.e., number of underground employees, number of surface employees, and coal production). Based on these models, a mine was classified into a "low-risk" or "high-risk" category and assigned with a fuzzy risk index. Risk probabilities were then computed to generate risk profiles and identify mines with potential hazards.

Results: NIOSH mine demographic features yielded a prediction performance with an AUC of 0.724 (95% CI 0.717-0.731) based on the last 31-years' mine data and an AUC of 0.738 (95% CI: 0.726, 0.749) on the last 16-years' mine data. Fuzzy risk score shows that risk is greatest in mines with an average of 621 underground employees and a production of 4,210,150 tons. The ratio of tons/employee maximizes the risk at 16,342.18 tons/employee.

Conclusion: It is possible to predict the risk of underground coal mines based on their employee demographics and optimizing the allocation and distribution of employees in coal mines can help minimize the risk of accidents and injuries.