Research on the Prediction of Coal Workers' Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study.

BACKGROUND

Coal workers' pneumoconiosis (CWP) is the most prevalent occupational disease that causes irreversible lung damage. Early prediction of CWP is the key to blocking the irreversible process of pulmonary fibrosis. The prediction of CWP based on imaging data and biomarker detection is constrained due to high cost and poor convenience.

OBJECTIVE

The study aimed to use easily detectable clinical data to construct a prediction model for CWP through machine learning (ML) methods.

METHODS

A prediction framework was established using a moderate-sized dataset and multidimensional clinical features, including occupational information, lung function parameters, and blood indicators. Six ML algorithms (light gradient boosting machine, random forest, extreme gradient boosting, categorical boosting, support vector machine, and logistic regression) were trained and evaluated using a stratified 5-fold cross-validation and a held-out test set. Hyperparameter optimization was performed using a unified Optuna-based strategy to ensure fair comparison across models. Model interpretability was assessed using Shapley Additive Explanation on top-performing models. In addition, an ablation analysis was conducted by retraining models after excluding job type to assess the independent predictive value of clinical biomarkers.

RESULTS

All 6 models achieved consistently high predictive performance, and the differences among the top-performing models were small on the test set. After Optuna-based optimization, light gradient boosting machine and categorical boosting achieved high test-set area under curve values (0.974 and 0.975, respectively), while extreme gradient boosting achieved the highest recall (0.926) and F1-score (0.952). Compared with the baseline models, hyperparameter optimization resulted in only minor performance changes, indicating robust prediction under the current feature set and evaluation protocol. Shapley Additive Explanation analysis consistently identified age, forced expiratory volume/forced vital capacity, and platelet count as key contributors to CWP risk prediction. The ablation analysis further showed that model performance remained strong after removing job type, supporting the independent predictive value of clinical features beyond occupational history.

CONCLUSIONS

The research results have confirmed the potential of combining simple multidimensional features with ML algorithms for predicting CWP and provided new ideas for early diagnosis and intervention of patients with CWP.