DocumentCode :
2134329
Title :
Self organizing neural network approach for identification of patients with Congestive Heart Failure
Author :
Elfadil, Nazar ; Ibrahim, Intisar
Author_Institution :
Dept. of Comput. Eng., Fahad Bin Sultan Univ., Tabuk, Saudi Arabia
fYear :
2011
fDate :
7-9 April 2011
Firstpage :
1
Lastpage :
6
Abstract :
A new technique for identification of patients with Congestive Heart Failure (CHF) from normal controls is investigated in this paper using spectral analysis and neural networks. The data used in this work is obtained from Massachusetts Institute of Technology (MIT) databases. A data set of 17 CHF and 53 normal subjects is used as original learning data set. A larger learning data set, which is obtained by simulating 1000 CHF and 1000 normal subjects according to the spectral features obtained from the original learning data, is used by the self organizing neural network.
Keywords :
learning (artificial intelligence); medical information systems; neural nets; patient diagnosis; congestive heart failure; learning data set; patients identification; self organizing neural network approach; spectral analysis; Accuracy; Aging; Artificial neural networks; Heart; Neurons; Organizing; Training; Congestive Heart Failure (CHF); Self Oorganizing Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Computing and Systems (ICMCS), 2011 International Conference on
Conference_Location :
Ouarzazate
ISSN :
Pending
Print_ISBN :
978-1-61284-730-6
Type :
conf
DOI :
10.1109/ICMCS.2011.5945658
Filename :
5945658
Link To Document :
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