Chronic Kidney Disease Early Diagnosis Enhancing by Using Data Mining Classification and Features Selection-Reference-Cited by-同舟云学术

Chronic Kidney Disease Early Diagnosis Enhancing by Using Data Mining Classification and Features Selection

Published:2021 Issue: Volume: Page:61-76
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Moreno-Sanchez Pedro A.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-69963-5_5

Reference29 articles.

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3. Keith, D.S., Nichols, G.A., Gullion, C.M., Brown, J.B., Smith, D.H.: Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization. Arch. Intern. Med. 164(6), 659–663 (2004). https://doi.org/10.1001/archinte.164.6.659

4. Levin, A., et al.: Prevalence of abnormal serum vitamin D, PTH, calcium, and phosphorus in patients with chronic kidney disease: results of the study to evaluate early kidney disease. Kidney Int. 71(1), 31–38 (2007). https://doi.org/10.1038/sj.ki.5002009

5. Liao, M.-T., Sung, C.-C., Hung, K.-C., Wu, C.-C., Lo, L., Lu, K.-C.: Insulin resistance in patients with chronic kidney disease. J. Biomed. Biotechnol. 2012, 1–12 (2012). https://www.hindawi.com/journals/bmri/2012/691369/. Accessed 05 Aug 2020

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