Publication Name	World Journal of Diabetes
Manuscript ID	115097
Country	China

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
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
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.
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; In press

Received	2025-10-09 00:23
Peer-Review Started	2025-10-10 00:05
First Decision by Editorial Office Director	2025-11-06 08:52
Return for Revision	2025-11-06 08:52
Revised	2025-11-18 13:31
Publication Fee Transferred	2025-11-21 09:07
Second Decision by Editor	2026-02-04 02:41
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-02-04 08:59
Articles in Press	2026-02-04 08:59
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-9358 (online)
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