A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers.

ABSTRACT: The dark and humid environment of underground coal mines had a detrimental effect on workers' skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specific

SUBMITTER: Zheng Z 

PROVIDER: S-EPMC9368504 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature