Using machine learning to develop Iraqi-specific spirometric reference equations.

ABSTRACT:

Background

Spirometry is essential for diagnosing and managing respiratory diseases. Accurate interpretation relies on reference equations that reflect population-specific lung function. Global equations, such as those from the Global Lung Function Initiative (GLI), may not suit all populations, including Iraqis.

Objective

To develop sex-specific spirometric reference equations for healthy Iraqi adults using machine learning (ML) and compare their performance with the GLI 2012 and 2022 equations.

Methods

This cross-sectional study included 3,959 healthy, nonsmoking Iraqi adults aged ≥18 years. Spirometry was performed per ATS/ERS guidelines. Five ML models (linear regression, random forest, support vector machine, gradient boosting machine (GBM), and k-nearest neighbo

SUBMITTER: Al-Qerem W 

PROVIDER: S-EPMC12599341 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature