ArticleFrontiers in medicine2025
Machine learning-based spirometry reference values for the Iranian population: a cross-sectional study from the Shahedieh PERSIAN cohort.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Competing risks of AIDS and death in people living with HIV: a left-truncated and right-censored retrospective cohort study in Khuzestan, Iran (2001-2022).BMC infectious diseases · 2026Article
- National, Subnational, and Risk-Attributed Burden of Chronic Respiratory Diseases in Iran (1990-2021): A Longitudinal Clustering Analysis of Global Burden of Disease 2021 Data.Health science reports · 2026Article
- High CALLY index is independently associated with lower odds of preserved ratio impaired spirometry (PRISm) and provides incremental predictive value: a retrospective observational study.Frontiers in medicine · 2026Article
- Using machine learning to develop Iraqi-specific spirometric reference equations.Future science OA · 2025Article
- Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach.BMC bioinformatics · 2025Article
- Training machine learning-based spirometry reference equations: a comparison with GAMLSS and GLI reference equations.European clinical respiratory journal · 2025Article
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5 authors.
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Abstract
Objective: This study aimed to determine spirometric norm values for the healthy Iranian adult population and compare them with established norm equations, specifically the GLI-Caucasian and Iranian equations. Methods: During the recruitment phase of the Shahedieh Prospective Epidemiological Research Studies in Iran (PERSIAN) in 2016, spirometric parameters of 998 participants were obtained. KNN regression was used to extract reference values for spirometric parameters FEV Results: This study included 473 female participants and 525 male participants. KNN regression provided more accurate predictions for four spirometric parameters than MLR and LMS. The MSE for predicting FVC in female participants was 0.159, 0.169, and 0.165 in KNN regression, MLR, and LMS, respectively. The predictions of the present study were closer to the actual values of the reference population for four indicators compared to the prediction values using two sets of reference equations. The MSE of predicted FVC for female participants was 0.159 in the present study, which was less than the Iranian (MSE = 0.344) and GLI-Caucasian (MSE = 0.397) equations. Conclusion: Using a flexible machine learning approach, this study established spirometry reference values specifically for the Iranian population. Recognizing that spirometry reference values vary among different populations, the Excel calculator developed in this research can be a valuable tool in healthcare centers for assessing lung function in Iranian adults.
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