期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2016
卷号：4
期号：1
页码：812
DOI：10.15680/IJIRCCE.2016.0401049
出版社：S&S Publications
摘要：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 object ive 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 us ing 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 Back - Propagation Neural Network, Radial Basis Funct ion and Random Forest . The experimenta l results show that the Radial b asis f unction algorithm gives better result than the other classification algorithms and produce s 85.3% accuracy
关键词：Chronic Kidney Disease (CKD); Data mining; Machine Learning (ML); Back-Propagation Neural Network; Radial Basis Function and Random Forest
