Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods.

ABSTRACT:

Background

Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In this paper, we focus on COPD in the United States from 2016 to 2019.

Objective

We gathered a diverse set of non-personally identifiable information from public data sources to better understand and predict COPD rates at the core-based statistical area (CBSA) level in the United States. Our objective was to compare linear models with machine learning models to obtain the most accurate and interpretable model of COPD.

Methods

We integrated non-personally identifiable information from multiple Centers for Disease Control and Prevention sources and used the

SUBMITTER: Kamis A 

PROVIDER: S-EPMC11393512 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature