Conference Proceeding

Diagnosis of Thyroid Disorders using Artificial Neural Networks

04/2009; DOI:10.1109/IADCC.2009.4809154 pp.1016 - 1020 In proceeding of: Advance Computing Conference, 2009. IACC 2009. IEEE International
Source: IEEE Xplore

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.

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Keywords

artificial neural networks
 
factors
 
feed-forward neural network
 
learning vector quantization
 
major problem
 
networks
 
paper presents
 
performance comparison
 
propagation algorithm
 
radial basis function
 
thyroid disorders
 
training time