1. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020; 395(10225):709–733.
https://doi.org/10.1016/S0140-6736(20)30045-3.
3. Barbieri C, Cattinelli I, Neri L, Mari F, Ramos R, Brancaccio D, et al. Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: proof of concept and first clinical assessment. Kidney Dis (Basel). 2019; 5(1):28–33.
https://doi.org/10.1159/000493479.
Article
7. Perl J, Dember LM, Bargman JM, Browne T, Charytan DM, Flythe JE, et al. The use of a multidimensional measure of dialysis adequacy-moving beyond small solute kinetics. Clin J Am Soc Nephrol. 2017; 12(5):839–47.
https://doi.org/10.2215/CJN.08460816.
Article
9. Banerjee A, Noor A, Siddiqua N, Uddin MN. Significance of attribute selection in the classification of chronic renal disease. In : Proceedings of 2019 2nd International Conference on Advanced Computational and Communication Paradigms (ICACCP); 2019 Feb 25–28; Gangtok, India. p. 1–6.
https://doi.org/10.1109/ICACCP.2019.8882937.
Article
12. Hueso M, de Haro L, Calabia J, Dal-Re R, Tebe C, Gibert K, et al. Leveraging data science for a personalized haemodialysis. Kidney Dis (Basel). 2020; 6(6):385–94.
https://doi.org/10.1159/000507291.
Article
13. Wickramasinghe MP, Perera DM, Kahandawaarachchi KA. Dietary prediction for patients with chronic kidney disease (CKD) by considering blood potassium level using machine learning algorithms. In : Proceedings of 2017 IEEE Life Sciences Conference (LSC); 2017 Dec 13–15; Sydney, Australia. p. 300–3.
https://doi.org/10.1109/LSC.2017.8268202.
Article
14. Maurya A, Wable R, Shinde R, John S, Jadhav R, Dakshayani R. Chronic kidney disease prediction and recommendation of suitable diet plan by using machine learning. In : Proceedings of 2019 International Conference on Nascent Technologies in Engineering (ICNTE); 2019 Jan 4–5; Navi Mumbai, India. p. 1–4.
https://doi.org/10.1109/ICNTE44896.2019.8946029.
Article
15. Amirgaliyev Y, Shamiluulu S, Serek A. Analysis of chronic kidney disease dataset by applying machine learning methods. In : Proceedings of 2018 IEEE 12th International Conference on Application of Information and Communication Technologies (AICT); 2018 Oct 17–19; Almaty, Kazakhstan. p. 1–4.
https://doi.org/10.1109/ICAICT.2018.8747140.
Article
16. Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci Rep. 2019; 9(1):11862.
https://doi.org/10.1038/s41598-019-48263-5.
Article