| ISSN |
1007-9327 (print) and 2219-2840 (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. 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 |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Fei-Xiang Xiong, Lei Sun, Xue-Jie Zhang, Jia-Liang Chen, Yang Zhou, Xiao-Min Ji, Pei-Pei Meng, Tong Wu, Xian-Bo Wang and Yi-Xin Hou |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Natural Science Foundation of China |
81970512 |
| Beijing Hospitals Authority Youth Programme |
QMl220201802 |
| Beijing Traditional Chinese Medicine Science and Technology Development Fund Project |
Qn-2020-25 |
| High-Level Public Health Technical Personnel Construction Project |
|
|
| Corresponding Author |
Yi-Xin Hou, PhD, Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, Chaoyang District, Beijing 100015, China. xuexin162@163.com |
| Key Words |
Machine learning; Advanced fibrosis; Non-alcoholic steatohepatitis; Extreme Gradient Boosting; Non-invasive |
| Core Tip |
This study employed Shapley Additive Explanations (SHAP) to select key features for diagnosing advanced liver fibrosis in non-alcoholic steatohepatitis patients. Among five machine learning models, the Extreme Gradient Boosting model achieved the best performance and was further developed into an online diagnostic tool. SHAP was also used to provide local explanations, clarifying its applicability across clinical populations. |
| Publish Date |
2025-02-18 09:00 |
| Citation |
<p>Xiong FX, Sun L, Zhang XJ, Chen JL, Zhou Y, Ji XM, Meng PP, Wu T, Wang XB, Hou YX. Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study. <i>World J Gastroenterol</i> 2025; 31(9): 101383</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i9/101383.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i9.101383 |
