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

Background The mortality burden of metabolic dysfunction-associated fatty liver disease (MAFLD) is rising, making it crucial to predict mortality and identify the factors influencing it. While advanced machine learning algorithms are gaining recognition as effective tools for clinical prediction, th...

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Veröffentlicht in:	BMC gastroenterology Jg. 25; H. 1; S. 376 - 14
Hauptverfasser:	Wang, Xueni, Chen, Huihui, Wang, Luqiao, Sun, Wenguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 15.05.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Adult Aged Algorithms All-cause mortality Biomarkers Body mass index Cause of Death Cigarettes Complications and side effects Data mining Diabetes Diseases Fatty liver Fatty Liver - mortality Feature selection Female Forecasts and trends Gastroenterology Glucose Health aspects Hepatology Humans Hypertension Internal Medicine Kaplan-Meier Estimate Learning algorithms Liver diseases Longitudinal Studies Machine Learning Male Medical laboratories Medical research Medicine Medicine & Public Health Medicine, Experimental Metabolic diseases Metabolic dysfunction-associated fatty liver disease Metabolism Methods Middle Aged Missing data Mortality Non-alcoholic Fatty Liver Disease - mortality Nutrition Surveys Obesity Patient outcomes Prognosis Quality of life Risk Factors Statistics Survival Type 2 diabetes Ultrasonic imaging United States United States > US All-cause mortality Fatty liver Metabolic dysfunction-associated fatty liver disease Machine learning
ISSN:	1471-230X, 1471-230X
Online-Zugang:	Volltext
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