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3/12/2026 9:31:56 AM | Browse: 9 | Download: 13
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2025-10-09 00:23 |
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2025-11-06 08:52 |
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2026-02-04 02:41 |
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2026-02-26 09:49 |
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2026-03-12 09:18 |
| ISSN |
1948-9358 (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) 2026. 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 |
Meta-Analysis |
| Article Title |
Machine learning and deep learning in predicting the risk of diabetic kidney disease: A systematic review and meta-analysis
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Qing Chen, Hua-Wei Peng, Chen-Xiao Fu, Kai-Kai Meng and Jun-Bei Zhang |
| Funding Agency and Grant Number |
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| Corresponding Author |
Jun-Bei Zhang, Department of Endocrinology, The Yiwu Central Hospital, No. 699 Jiangdong Road, Yiwu 322000, Zhejiang Province, China. e1677716412@126.com |
| Key Words |
Diabetic kidney disease; Type 2 diabetes mellitus; Predicting; Deep learning; Machine learning; Artificial intelligence |
| Core Tip |
Machine learning and deep learning algorithms show great performance in predicting diabetic kidney disease (DKD) among type 2 diabetes mellitus patients. Predictors, such as age, body mass index, estimated glomerular filtration rate, serum creatinine, urinary albumin, glycated hemoglobin, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides, play significant roles in DKD prediction. Models employing cross-validation methods exhibit superior predictive capability for DKD compared to those using holdout validation approaches. |
| Publish Date |
2026-03-12 09:18 |
| Citation |
Chen Q, Peng HW, Fu CX, Meng KK, Zhang JB. Machine learning and deep learning in predicting the risk of diabetic kidney disease: A systematic review and meta-analysis. World J Diabetes 2026; 17(3): 115097 |
| URL |
https://www.wjgnet.com/1948-9358/full/v17/i3/115097.htm |
| DOI |
https://dx.doi.org/10.4239/wjd.v17.i3.115097 |
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