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

Volume

15

fYear

2010

fDate

22-24 Oct. 2010

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICCASM.2010.5622616

Filename

5622616