Insight into Best Variables for COPD Case Identification: A Random Forests Analysis.

ABSTRACT:

Rationale

This study is part of a larger, multi-method project to develop a questionnaire for identifying undiagnosed cases of chronic obstructive pulmonary disease (COPD) in primary care settings, with specific interest in the detection of patients with moderate to severe airway obstruction or risk of exacerbation.

Objectives

To examine 3 existing datasets for insight into key features of COPD that could be useful in the identification of undiagnosed COPD.

Methods

Random forests analyses were applied to the following databases: COPD Foundation Peak Flow Study Cohort (N=5761), Burden of Obstructive Lung Disease (BOLD) Kentucky site (N=508), and COPDGene® (N=10,214). Four scenarios were examined to find the best, smallest sets of variables that distinguished cases and

SUBMITTER: Leidy NK 

PROVIDER: S-EPMC4729451 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature