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...

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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

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