Diagnosis of Thyroid Disorders using Artificial Neural Networks

Author

Shukla, Anupam ; Tiwari, Ritu ; Kaur, Prabhdeep ; Janghel, R.R.

Author_Institution

ICT Dept., ABV-IIITM, Gwalior

fYear

2009

fDate

6-7 March 2009

Firstpage

1016

Lastpage

1020

Abstract

A major problem in medical science is attaining the correct diagnosis of disease in precedence of its treatment. This paper presents the diagnosis of thyroid disorders using artificial neural networks (ANNs). The feed-forward neural network has been trained using three ANN algorithms; the Back propagation algorithm (BPA), the radial basis function (RBF) Networks and the learning vector quantization (LVQ) networks. The networks are simulated using MATLAB and their performance is assessed in terms of factors like accuracy of diagnosis and training time. The performance comparison helps to find out the best model for diagnosis of thyroid disorders.

Keywords

backpropagation; diseases; medical diagnostic computing; patient diagnosis; patient treatment; radial basis function networks; vector quantisation; artificial neural network training; back propagation algorithm; disease diagnosis treatment; learning vector quantization; radial basis function network; thyroid disorder; Artificial neural networks; Back; Diseases; Feedforward neural networks; Feedforward systems; MATLAB; Medical diagnostic imaging; Medical treatment; Neural networks; Vector quantization; Artificial Neural Networks; Backpropagation Networks; Diagnosis; Learning Vector Quantization Networks; Radial Basis Function Networks; Thyroid disorders;

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference, 2009. IACC 2009. IEEE International

Conference_Location

Patiala

Print_ISBN

978-1-4244-2927-1

Electronic_ISBN

978-1-4244-2928-8

Type

conf

DOI

10.1109/IADCC.2009.4809154

Filename

4809154