Innovative machine learning-based prediction of early airway hyperresponsiveness using baseline pulmonary function parameters
This study identifies MEF75%, MMEF75-25%, FEV1/FVC%, and PEF% as effective predictors of early airway hyperresponsiveness in suspected asthma patients. The machine learning-based predictive model demonstrates strong performance and clinical utility, offering potential as a visual tool for early detection and standardized treatment, thereby reducing the risk of symptom exacerbation, lung function decline, and airway remodeling.
Authors
- Guihua Zhao (ORCID: https://orcid.org/0000-0002-4739-4356)
- Zhuochang Chen (ORCID: https://orcid.org/0000-0002-4172-0894)
- Xingru Zhao (ORCID: https://orcid.org/0000-0002-5577-4965)
- Hua Yang (ORCID: https://orcid.org/0000-0001-6720-4831)
- Lihong Yang (ORCID: https://orcid.org/0000-0003-0141-008X)
- Jinyi Xu
- Chenxiao Xu
Institutions
- Zhengzhou University (CN)
- Henan Provincial People's Hospital (CN)
- Zhengzhou People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-04
- DOI
- https://doi.org/10.3389/fmed.2025.1611683
- Citations
- 1
- Primary Topic
- Asthma and respiratory diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.16