Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms.

ABSTRACT:

Purpose

The purpose of this study is to propose an efficient coal workers' pneumoconiosis (CWP) clinical prediction system and put it into clinical use for clinical diagnosis of pneumoconiosis.

Methods

Patients with CWP and dust-exposed workers who were enrolled from August 2021 to December 2021 were included in this study. Firstly, we chose the embedded method through using three feature selection approaches to perform the prediction analysis. Then, we performed the machine learning algorithms as the model backbone and combined them with three feature selection methods, respectively, to determine the optimal predictive model for CWP.

Results

Through applying three feature selection approaches based on machine learning algorithms, it was found that AaDO₂ an

SUBMITTER: Dong H 

PROVIDER: S-EPMC10363790 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature