Title :
Application of radial basis function neural network to estimate glomerular filtration rate in Chinese patients with chronic kidney disease
Author :
Xun, Liu ; Xiaoming, Wu ; Ningshan, Li ; Tanqi, Lou
Author_Institution :
Dept. of Internal Med., Third Affiliated Hosp. of Sun Yet-sun Univ., Guangzhou, China
Abstract :
Artificial neural networks have been widely used in the field of engineering forecasting. This paper attempts to predict glomerular filtration rate (GFR) in Chinese patients with chronic kidney disease (CKD) by radial basis function (RBF) neural network. Our data indicated that when serum creatinine (SC) was measured by the enzymatic method, based on both overall performance as well as performance in different CKD stages, this RBF network model is suitable for the specific Chinese population tested.
Keywords :
diseases; kidney; medical computing; patient treatment; radial basis function networks; Chinese patient; RBF network model; artificial neural network; chronic kidney disease; engineering forecasting; enzymatic method; glomerular filtration rate; radial basis function neural network; serum creatinine; Equations; Logic gates; Radial basis function networks; USA Councils; artificial neural network; chronic kidney disease; glomerular filtration rate; radial basis function; serum creatinine;
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
DOI :
10.1109/ICCASM.2010.5622616
