Privacy-preserving predictive modeling for early detection of chronic kidney disease
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13721-024-00452-7.pdf
Reference43 articles.
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2. Ani R, Sasi G, Sankar UR, Deepa O (2016) Decision support system for diagnosis and prediction of chronic renal failure using random subspace classification. In: Paper presented at the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 1287–1292
3. Chahar V, Katoch S, Chauhan S (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80. https://doi.org/10.1007/s11042-020-10139-6
4. Chen TK, Knicely DH, Grams ME (2019) Chronic kidney disease diagnosis and management: a review. JAMA 322(13):1294–1304. https://doi.org/10.1001/jama.2019.14745
5. Dare AJ, Fu SH, Patra J, Rodriguez PS, Thakur JS, Jha P (2017). Million Death Study Collaborators. Renal failure deaths and their risk factors in India 2001-13: nationally representative estimates from the Million Death Study. Lancet Glob Health. 5(1):e89-e95. https://doi.org/10.1016/S2214-109X(16)30308-4
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