Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references

Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references

Abstract Introduction Spirometry is associated with several diagnostic difficulties, and as a result, misdiagnosis of chronic obstructive pulmonary disease (COPD) occurs. This study aims to investigate how random forest (RF) can be used to improve the existing clinical FVC and FEV1 reference values...

Full description

Bibliographic Details
Main Authors: Kris Kristensen, Pernille H. Olesen, Anna K. Roerbaek, Louise Nielsen, Helle K. Hansen, Simon L. Cichosz, Morten H. Jensen, Ole Hejlesen
Format: Article
Language:English
Published: Wiley 2023-08-01
Series:The Clinical Respiratory Journal
Subjects:
clinical references
COPD
misdiagnosis
multiple linear regression
random forest
spirometry
Online Access:https://doi.org/10.1111/crj.13662

Internet

https://doi.org/10.1111/crj.13662

Similar Items