Publication Name	World Journal of Hepatology
Manuscript ID	65432
Country	United States

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database
Manuscript Source	Invited Manuscript
All Author List	Amporn Atsawarungruangkit, Passisd Laoveeravat and Kittichai Promrat
Funding Agency and Grant Number
Corresponding Author	Amporn Atsawarungruangkit, MD, Academic Fellow, Instructor, Research Fellow, Division of Gastroenterology, Warren Alpert Medical School, Brown University, 593 Eddy Street, POB 240, Providence, RI 02903, United States. amporn_atsawarungruangkit@brown.edu
Key Words	Artificial intelligence; Machine learning; Non-alcoholic fatty liver disease; Fatty liver; United States population; NHANES
Core Tip	A simple method with a good accuracy for identifying patients with non-alcoholic fatty liver disease is highly desirable. Among 24 machine learning models, the ensemble of random undersampling boosted trees was the top performer (accuracy 71.1% and F1 0.56). A simple model (coarse trees) with only two predictors (fasting C-peptide and waist circumference) had an accuracy of 74.9% and an F1 of 0.33. Not every machine learning model is complex. Using a simple model such as coarse trees, physicians can easily integrate machine learning model into their practice without any software implementation.
Citation	Atsawarungruangkit A, Laoveeravat P, Promrat K. Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database. World J Hepatol 2021; 13(10): 1417-1427

Received	2021-03-07 02:44
Peer-Review Started	2021-03-07 02:47
To Make the First Decision
Return for Revision	2021-05-02 05:34
Revised	2021-05-11 03:56
Second Decision	2021-09-17 03:30
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-09-19 09:04
Articles in Press	2021-09-19 09:04
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-10-20 01:41

ISSN	1948-5182 (online)
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