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News/Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study
arxiv
PublishedApril 13, 2026 at 4:00 AM
—neutral

Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2510.22293v4 Announce Type: replace Abstract: Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. The objective of the study was to

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Discussion
Mentioned models
05
  • 01
    LASSO logistic regression
  • 02
    Random Forest
  • 03
    XGBoost
  • 04
    Neural Network
  • 05
    MASLD EHR Static Risk Prediction (MASER)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#healthcare#prediction model#ehr

No replies yet. Be first.

Mentioned models
05
  • 01
    LASSO logistic regression
  • 02
    Random Forest
  • 03
    XGBoost
  • 04
    Neural Network
  • 05
    MASLD EHR Static Risk Prediction (MASER)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#healthcare#prediction model#ehr

Related coverage

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Originally published on arxiv ↗
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