A Novel Approach to Prognosticate CKD Using a Supervised and Unsupervised Learning Algorithms-Reference-Cited by-同舟云学术

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A Novel Approach to Prognosticate CKD Using a Supervised and Unsupervised Learning Algorithms

Published:2023 Issue: Volume: Page:107-116
ISSN:2190-3018
Container-title:Intelligent Manufacturing and Energy Sustainability
language:
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Author:

Ashwathi S.,Swamy Goud A.,Niranjan L.,Sreekantha B.,Suneetha J.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-8497-6_11

Reference15 articles.

1. Ani, R., Sasi, G., Sankar, U.R., Deepa, O.S.: Decision support system for diagnosis and prediction of chronic renal failure using random subspace classification. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016). https://doi.org/10.1109/icacci.2016.7732224

2. Gupta, B., Rawat, A., Jain, A., Arora, A., Dhami, N.: Analysis of various decision tree algorithms for classification in data mining. Int. J. Comput. Appl. 163(8), 15–19. https://doi.org/10.5120/ijca2017913660

3. Tangri, N., Kitsios, G.D., Inker, L.A., Griffith, J., Naimark, D.M., Walker, S., Levey, A.S.: Risk prediction models for patients with chronic kidney disease: a systematic review. Ann. Intern. Med. 158(8), 596–603 (2013)

4. Owens, C.D., Ho, K.J., Kim, S., Schanzer, A., Lin, J., Matros, E., Belkin, M., Conte, M.S.: Refinement of survival prediction in patients undergoing lower extremity bypass surgery: stratification by chronic kidney disease classification. J. Vascular Surg. 45(5), 944–952 (2007)

5. Niranjan, L., Priyatham, M.M.: Lifetime ratio improvement technique using special fixed sensing points in wireless sensor network. Int. J. Pervasive Comput. Commun. 17(5), 483–508 (2021).https://doi.org/10.1108/IJPCC-10-2020-0165

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