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

ABSTRACT:

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 boos

SUBMITTER: Li H 

PROVIDER: S-EPMC12904349 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature