Predicting Development of Chronic Obstructive Pulmonary Disease and its Risk Factor Analysis

TL;DR

This study employs machine learning to predict COPD development by integrating sociodemographic, clinical, and genetic data, aiming to identify key risk factors beyond smoking.

Contribution

It introduces a comprehensive machine learning approach combining multiple data types to improve COPD risk prediction and identify novel risk factors.

Findings

Machine learning models achieved high prediction accuracy.

Multiple risk factors, including genetic markers, were identified.

Enhanced understanding of COPD development mechanisms.

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is an irreversible airway obstruction with a high societal burden. Although smoking is known to be the biggest risk factor, additional components need to be considered. In this study, we aim to identify COPD risk factors by applying machine learning models that integrate sociodemographic, clinical, and genetic data to predict COPD development.