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An Artificial Neural Network Model for Neonatal Disease Diagnosis
Dilip Roy Chowdhury, Mridula Chatterjee, R. K. Samanta
Pages - 96 - 106     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 2   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
Artificial IntelligenceNeural Network , Multi Layer Perceptron , Neonate, Back Propagation
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
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Mr. Dilip Roy Chowdhury
University of North Bengal - India
Professor Mridula Chatterjee
Department of Pediatrics - India
Professor R. K. Samanta
University of North Bengal - India

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