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
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Zusammenfassung: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, their ability to predict all-cause mortality of MAFLD individuals remains uncertain. This study aimed to develop different machine learning models to predict all-cause mortality of MAFLD individuals, compare the predictive performance of these models, and identify the risk factors contributing all-cause mortality, which is crucial for management of MAFLD individuals. Methods We included 3921 MAFLD individuals in NHANES III. After a median follow-up time of 310 months, 1815 (46.3%) deaths were recorded. The data (demographic, behavioral factors and laboratory indicators) were utilized to construct machine learning models (Coxnet, RSF, GBS) after feature selection. Time-dependent AUC, time-dependent brier and C-index were then evaluated the performance of models. We identified the top five factors that contributed significantly to all-cause mortality and further explore the association with all-cause mortality using RCS and Kaplan–Meier survival curves. Results Coxnet showed the best performance in short-term and long-term predictions with time-dependent AUC of 0.82 at 5 years and 0.88 at 25 years. Age, FORNS, waist circumstance, AAR, FLI were associated positively with all-cause mortality. Compared to the individuals who smoked more than 100 cigarettes, those below 100 had better survival outcome ( P  < 0.0001). Conclusions 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.
Bibliographie:ObjectType-Article-1
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ISSN:1471-230X
1471-230X
DOI:10.1186/s12876-025-03946-4