Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/1329
Title: DIAGNOSIS OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING ALGORITHMS
Authors: Radha N
Ramya S
Keywords: Chronic Kidney Disease (CKD)
Data mining
Machine Learning (ML)
Back-Propagation Neural Network
Radial Basis Function and Random Forest
Issue Date: Jan-2016
Publisher: International Journal of Innovative Research in Computer and Communication Engineering
Abstract: Chronic Kidney Disease (CKD) is a gradual decrease in renal function over a period of several months or years. Diabetes and high blood pressure are the most common causes of chronic kidney disease. The main objective of this work is to determine the kidney function failure by applying the classification algorithm on the test result obtained from the patient medical report. The aim of this work is to reduce the diagnosis time and to improve the diagnosis accuracy using classification algorithms. The proposed work deals with classification of different stages in chronic kidney disease according to its severity. The experiment is performed on different algorithms like Backpropagation Neural Network, Radial Basis Function and Random Forest. The experimental results show that the Radial basis function algorithm gives better result than the other classification algorithms and produces 85.3% accuracy
URI: http://www.ijircce.com/upload/2016/january/49_3_Diagnosis.pdf
http://localhost:8080/xmlui/handle/123456789/1329
ISSN: Print:2320-9798
Online:2320-9801
Appears in Collections:International Journals

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