An explainable artificial intelligence framework for risk prediction of COPD in smokers.

ABSTRACT:

Background

Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions.

Methods

The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMO

SUBMITTER: Wang X 

PROVIDER: S-EPMC10626705 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature