Prediction of Abnormality in Kidney Function Using Classification Techniques and Fuzzy Systems
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9521-9_6
Reference41 articles.
1. Lakshmi Prasudha M, Kasumolla R, Sukheja D (2021) Research reviews: towards identification and classification kidney disease using computational technology. In: 2021 5th international conference on computing methodologies and communication (ICCMC), pp 1387–1391. https://doi.org/10.1109/ICCMC51019.2021.9418454
2. Prasudha ML et al (2021) Comprehensive analysis of state-of-the-art CAD tools and techniques for chronic kidney disease (CKD). IJBDAH 6(2):1–12. https://doi.org/10.4018/IJBDAH.287605
3. Sivasankar E, Pradeep R, Sinandham S (2019) Identification of important biomarkers for detection of chronic kidney disease using feature selection and classification algorithms. Int J Med Eng Inform 11(4)
4. Aditya K, Babita P (2020) “A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease. Int J Data Anal Tech Strateg (IJDATS) 12(2)
5. Pramila A, Eswaran P (2021) An efficient oppositional crow search optimization-based deep neural network classifier for chronic kidney disease identification. Int J Innov Comput Appl (IJICA) 12(4)
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