Predicting diabetic nephropathy in type 2 diabetic patients using machine learning algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Endocrinology, Diabetes and Metabolism,Internal Medicine
Link
https://link.springer.com/content/pdf/10.1007/s40200-022-01076-2.pdf
Reference47 articles.
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2. Zhao N, Gao JD, Zhang HL. Research progress of glycosylated haemoglobin as a diagnostic criterion for diabetes. Chin Clin Res. 2015;1:127–8.
3. Ahmad J. Management of diabetic nephropathy: recent progress and future perspective. Diabetes Metab Syndr Clin Res Rev. Elsevier; 2015;9:343–358.
4. Radcliffe NJ, Seah J, Clarke M, MacIsaac RJ, Jerums G, Ekinci EI. Clinical predictive factors in diabetic kidney disease progression. J Diabetes Investig. Wiley Online Library; 2017;8:6–18.
5. Narres M, Claessen H, Droste S, Kvitkina T, Koch M, Kuss O, et al. The incidence of end-stage renal disease in the diabetic (compared to the non-diabetic) population: a systematic review. PLoS One. Public Library of Science San Francisco, CA USA; 2016;11:e0147329.
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