ArticleFuture science OA2025
Using machine learning to develop Iraqi-specific spirometric reference equations.
Article in Future science OA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
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,
resultsGBM outperformed all other models and GLI equations. In females,
conclusionGBM-derived equations show improved accuracy and calibration over GLI standards for Iraqi adults, offering a more suitable tool for spirometry interpretation.
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