

Analysis of Chronic Kidney Disease Using Machine Learning
Abstract
Keywords
References
Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, et al. Global prevalence of chronic kidney disease—A systematic review and meta-analysis. PloS one. 2016;11(7):e0158765. pmid:27383068
Tsai MH, Hsu CY, Lin MY, Yen MF, Chen HH, Chiu YH, et al. Incidence, prevalence, and duration of chronic kidney disease in Taiwan: Results from a community-based screening program of 106,094 individuals. Nephron. 2018;140(3):175– 184. pmid:30138926
Wu MY, Wu MS. Taiwan renal care system: A learning health-care system. Nephrology. 2018;23:112–115.
Eknoyan G, Lameire N, Barsoum R, Eckardt KU, Levin A, Levin N, et al. The burden of kidney disease: Improving global outcomes. Kidney International. 2004;66(4):1310– 1314. pmid:15458424
Saran R, Robinson B, Abbott KC, Agodoa LY, Albertus P, Ayanian J, et al. US renal data system 2016 annual data report: Epidemiology of kidney disease in the United States.
Gøransson LG, Bergrem H. Consequences of late referral of patients with end-stage renal disease. Journal of Internal Medicine. 2001;250(2):154–159.
Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: Global dimension and perspectives. The Lancet. 2013;382(9888):260–272.
United States Renal Data System. 2015 USRDS annual data report: Epidemiology of kidney disease in the United States; 2015.
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 3.0 License.