Training machine learning-based spirometry reference equations: a comparison with GAMLSS and GLI reference equations.

ABSTRACT:

Introduction

Interpretation of spirometry data depends on the availability of reference equations that reflect the physiological norms of the assessed population. Although GAMLSS models provide clinically acceptable models, they may lack simplicity and ease of application. This study evaluated the efficiency of machine learning (ML)-based spirometry reference equations as an alternative for Jordanian adults.

Method

In this cross-sectional study, ML models were trained using age and height to predict FEV₁, FVC and FEV₁/FVC. Model development was based on the same datasets previously used to construct GAMLSS-based Jordanian equations, which included 1,948 participants (54.2% females). External validation was performed on a newly recruited sample of healthy, non-smoking adults

SUBMITTER: Al-Qerem W 

PROVIDER: S-EPMC12498361 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature