A novel enhanced decision tree model for detecting chronic kidney disease
Author:
Publisher
Springer Science and Business Media LLC
Subject
Urology
Link
https://link.springer.com/content/pdf/10.1007/s13721-021-00302-w.pdf
Reference101 articles.
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3. Al-Hadeethi H, Abdulla S, Diykh M, Deo RC, Green JH (2020) Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications. Expert Syst Appl 161:113676
4. Aljaaf AJ, Al-Jumeily D, Haglan HM, Alloghani M, Baker T, Hussain AJ, Mustafina J (2018). Early prediction of chronic kidney disease using machine learning supported by predictive analytics. In: 2018 IEEE congress on evolutionary computation (CEC). IEEE, New York, pp 1–9
5. Alloghani M, Al-Jumeily D, Hussain A, Liatsis P, Aljaaf AJ (2020) Performance-based prediction of chronic kidney disease using machine learning for high-risk cardiovascular disease patients. Nature-inspired computation in data mining and machine learning. Springer, Cham, pp 187–206
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