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