A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-022-09759-y.pdf
Reference27 articles.
1. Thomas, R., Kanso, A., & Sedor, J. R. (2008). Chronic kidney disease and its complications. Primary Care: Clinics in Office Practice, 15(2), 329–344. https://doi.org/10.1016/j.pop.2008.01.008
2. Kriplani, H., Patel, B., & Roy, S. (2019). Prediction of chronic kidney diseases using deep artificial neural network technique. Computer aided intervention and diagnostics in clinical and medical images (pp. 179–187). Cham: Springer. https://doi.org/10.1007/978-3-030-04061-1_18
3. Chang, H. L., Wu, C. C., Lee, S. P., Chen, Y. K., Su, W., & Su, S. L. (2019). A predictive model for progression of CKD. Medicine, 98(26), e15186. https://doi.org/10.1097/MD.0000000000016186
4. https://kdigo.org/wp-content/uploads/2017/02/KDIGO_2012_CKD_GL.pdf (Accessed on: 09–08–2021)
5. Rady, E.-H., & Anwar, A. S. (2019). Prediction of kidney disease stages using data mining algorithms. Open Access Journal on Informatics in Medicine Unlocked. https://doi.org/10.1016/j.imu.2019.100178
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