Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES

Machine learning has a promising application in predicting all-cause mortality in MAFLD individuals. Combined the results of interpretable machine learning and association analyses, we found risk factors which contributing to the all-cause mortality. These findings provide insights for community health practitioners to intervene in modifiable risk factors, thereby improving the survival and quality of life of MAFLD individuals.

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Publication Details

Journal
BMC Gastroenterology
Published
2025-05-15
DOI
https://doi.org/10.1186/s12876-025-03946-4
Citations
4
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
5.42
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article

Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES

X. Wang, Luqiao Wang, Wenguang Sun, Huihui Chen
4 citations
BMC Gastroenterology
Liver Disease Diagnosis and Treatment
5.42
article

Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES

X. Wang, Luqiao Wang, Wenguang Sun, Huihui Chen
article en
4 citations

Abstract

Machine learning has a promising application in predicting all-cause mortality in MAFLD individuals. Combined the results of interpretable machine learning and association analyses, we found risk factors which contributing to the all-cause mortality. These findings provide insights for community health practitioners to intervene in modifiable risk factors, thereby improving the survival and quality of life of MAFLD individuals.

BMC GastroenterologyVol. 25(1)
Second Affiliated Hospital of Zhejiang University (CN)
Good health and well-being
Openalex Percentile: Top 4%
Liver Disease Diagnosis and Treatment
5.42
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