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Articles Published Processes
10/22/2021 1:53:28 PM | Browse: 559 | Download: 1248
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Received |
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2021-03-07 02:44 |
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Peer-Review Started |
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2021-03-07 02:47 |
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To Make the First Decision |
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Return for Revision |
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2021-05-02 05:34 |
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Revised |
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2021-05-11 03:56 |
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Second Decision |
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2021-09-17 03:30 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2021-09-19 09:04 |
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Articles in Press |
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2021-09-19 09:04 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2021-10-20 01:41 |
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Publish the Manuscript Online |
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2021-10-22 13:53 |
| ISSN |
1948-5182 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Amporn Atsawarungruangkit, Passisd Laoveeravat and Kittichai Promrat |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2021-10-22 13:53 |
| 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 |
| URL |
https://www.wjgnet.com/1948-5182/full/v13/i10/1417.htm |
| DOI |
https://dx.doi.org/10.4254/wjh.v13.i10.1417 |
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