Machine Learning Predicts Coal Workers' Pneumoconiosis

Researchers from Chinese medical and engineering institutions (published 2026) trained six machine-learning models on occupational history, lung-function tests, and routine blood markers to predict coal workers’ pneumoconiosis (CWP). Optuna-tuned LightGBM and CatBoost achieved test AUCs of 0.974 and 0.975, while XGBoost reached recall 0.926 and F1 0.952; SHAP highlighted age, FEV1/FVC, and platelet count as top predictors, and performance remained strong without job-type data.