Development of a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease: A secondary analysis of cross-sectional national survey.

ABSTRACT:

Objective

This study aims to develop and validate a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease (COPD) using data from a national survey.

Methods

Data from the Korea National Health and Nutrition Examination Survey (2007-2018) were used to extract 5466 COPD-eligible cases. The data collection involved demographic, behavioral, and clinical variables, including 21 predictors such as age, sex, and pulmonary function test results. The dependent variable, smoking status, was categorized as smoker or nonsmoker. A residual neural network (ResNN) model was developed and compared with five machine learning algorithms (random forest, decision tree, Gaussian Naive Bayes, K-nearest neighbor, and AdaBoost) and two deep learning mod

SUBMITTER: Pant S 

PROVIDER: S-EPMC12035114 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature