Machine learning and deep learning in predicting the risk of diabetic kidney disease: A systematic review and meta-analysis

doi:10.4239/wjd.v17.i3.115097

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Mar 15, 2026 (publication date) through Mar 15, 2026

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World Journal of Diabetes

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1948-9358

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Diabetes. Mar 15, 2026; 17(3): 115097
Published online Mar 15, 2026. doi: 10.4239/wjd.v17.i3.115097

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Figure 1 PRISMA flow diagram.

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Figure 2 Pooled area under the receiver operating characteristic curve values in internal validation sets. HK: Hausman-Kuipers; CI: Confidence interval.

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Figure 3 Subgroup analysis comparing cross-validation and holdout validation methods. CI: Confidence interval.

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