Using Machine Learning to Develop Iraqi-Specific Spirometric Reference Equations

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 neighbors) were trained using age and height. Data were split into training (70%) and validation (30%) sets. Performance was assessed using RMSE, R ², and z-score calibration. GBM was selected as the best model. Results: GBM outperformed all other models and GLI equations. In females, R ² was 0.4473 for FEV ₁ 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. Conclusion: GBM-derived equations show improved accuracy and calibration over GLI standards for Iraqi adults, offering a more suitable tool for spirometry interpretation.