Machine learning-based prediction of metabolic dysfunction-associated steatotic liver disease using National Health and Nutrition Examination Survey (NHANES) data

The prediction model developed using the XGBoost algorithm and the 12 selected features demonstrates high efficiency and stability in assessing MASLD risk. This model offers innovative technical solutions and data-driven support for the clinical early identification of high-risk populations, with the potential to optimize and refine MASLD prevention and control strategies.

Authors

Yangfan Fei (ORCID: https://orcid.org/0000-0002-0447-8672)
Xinyu Zhang (ORCID: https://orcid.org/0000-0001-8154-9334)
Xiang Liu (ORCID: https://orcid.org/0000-0002-8451-3465)
Yong Zhang (ORCID: https://orcid.org/0000-0003-0513-8941)
Xiaoxu Li (ORCID: https://orcid.org/0000-0003-4060-0935)

Institutions

Sichuan University (CN)
Meizhou City People's Hospital (CN)
West China Hospital of Sichuan University (CN)

Publication Details

Journal: PLoS ONE
Published: 2025-11-12
DOI: https://doi.org/10.1371/journal.pone.0335656
Primary Topic: Liver Disease Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

Machine learning-based prediction of metabolic dysfunction-associated steatotic liver disease using National Health and Nutrition Examination Survey (NHANES) data

Authors

Institutions

Publication Details

Machine learning-based prediction of metabolic dysfunction-associated steatotic liver disease using National Health and Nutrition Examination Survey (NHANES) data

Machine learning-based prediction of metabolic dysfunction-associated steatotic liver disease using National Health and Nutrition Examination Survey (NHANES) data

Abstract

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