Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.

ABSTRACT:

Background

The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features.

Methods

We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene^®) study. We constructed linear regression (LR) and supervised random forest models to predict 5-year progression in forced expiratory in 1 second (FEV₁) from 46 baseline features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in th

SUBMITTER: Boueiz A 

PROVIDER: S-EPMC9448009 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature