Prediction of the Preclinical Stage of Coal Workers' Pneumoconiosis on Nonimaging Data Integrating Prior Knowledge and Machine Learning.

Journal: Journal of occupational and environmental medicine
Published Date: Jan 12, 2026

Abstract

OBJECTIVE: This study aims to establish machine learning models using nonimaging data from health examinations of coal workers, which can screen the preclinical stage of coal workers' pneumoconiosis (CWP). METHODS: Nonimaging data from two centers, totaling 34,362 coal miners, were collected. From 84 initial variables, 19 were preliminarily screened, and LASSO selected eight key features. Six machine learning models were trained to predict the preclinical stage of CWP, evaluated using ROC curve. RESULTS: In the internal test set, GB achieved the best discrimination (AUC 88.19%), while DT yielded the highest accuracy (81.09%) and specificity (80.90%). In the external validation set, GB remained the top model by AUC (83.94%) and showed high sensitivity (87.67%). CONCLUSIONS: Age, FEV1, FEV1%, drinking status, smoking status, FVC, occupational category, and cumulative years of service are significant features for predicting the preclinical stage of CWP.

Authors

  • Fengtao Cui
    Occupational Health Surveillance and Management Center, Occupational Disease Prevention and Control Institute, Huaibei Mining Co., Ltd., Huaibei, 235000, China.
  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.
  • Yankun Ma
    Environment and Non-Communicable Disease Research Center, School of Public Health, China Medical University, Shenyang, 110122, China.
  • Kai Han
    Geneis Beijing Limited Company, Beijing 100102, China.
  • Bi Li
  • Fuhai Shen
    School of Public Health, North China University of Science and Technology, Tangshan, 063210, China.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.

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