.png){kind=icon}
Return
Using machine learning to develop Iraqi-specific spirometric reference equations
.png){kind=icon}
PRE
AI
DOI：10.1080/20565623.2025.2582429.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
BackgroundSpirometry 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.ObjectiveTo 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.MethodsThis 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 neighbors) were trained using age and height. Data were split into training (70%) and validation (30%) sets. Performance was assessed using RMSE, R2, and z-score calibration. GBM was selected as the best model.ResultsGBM outperformed all other models and GLI equations. In females, R2 was 0.4473 for FEV1 and 0.4519 for FVC; in males, 0.3509 and 0.3674, respectively. GLI equations underestimated lung volumes, while GBM predictions were well calibrated with mean z-scores near zero.ConclusionGBM-derived equations show improved accuracy and calibration over GLI standards for Iraqi adults, offering a more suitable tool for spirometry interpretation.
Keywords:
Spirometry
machine learning
reference equations
gradient boosting
Iraq
GLI
Pulmonary function
Journal
F
IF:
2.1
Papers:
160
Citations:
0
Organization
View more organizations
Cited Papers
ShareSaveShareSaveShareSave
ShareSaveShareSaveDetecting respiratory diseases using machine learning-based pattern recognition on spirometry data
OAAI
OAAIShareSaveEvaluation of the Global Lung Function Initiative 2012 reference values for spirometry in an Iranian population.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSaveThe recent multi-ethnic global lung initiative 2012 (GLI2012) reference values don't reflect contemporary adult's North African spirometryRESPIRATORY MEDICINEOAAI
RESPIRATORY MEDICINEIF3.1
OAAIShareSaveShareSave View more