Predicting total lung capacity from spirometry: a machine learning approach.

ABSTRACT:

Background and objective

Spirometry patterns can suggest that a patient has a restrictive ventilatory impairment; however, lung volume measurements such as total lung capacity (TLC) are required to confirm the diagnosis. The aim of the study was to train a supervised machine learning model that can accurately estimate TLC values from spirometry and subsequently identify which patients would most benefit from undergoing a complete pulmonary function test.

Methods

We trained three tree-based machine learning models on 51,761 spirometry data points with corresponding TLC measurements. We then compared model performance using an independent test set consisting of 1,402 patients. The best-performing model was used to retrospectively identify restrictive ventilatory impairment in

SUBMITTER: Beverin L 

PROVIDER: S-EPMC10238228 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature