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| An Artificial Neural Network Model for Neonatal Disease Diagnosis
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Source |
International Journal of Artificial Intelligence and Expert Systems (IJAE) |
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Table of Contents |
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Complete Issue PDF(2.42MB) |
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Volume: 2 Issue: 3 |
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Pages: 96-149 |
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Publication
Date: July / August 2011 |
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ISSN
(Online): 2180-124X |
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Pages |
96 - 106 |
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Author(s) |
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Published
Date |
05-08-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Artificial IntelligenceNeural Network , Multi Layer Perceptron , Neonate, Back Propagation |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Google Scholar |
| 3. Scribd |
| 4. Docstoc |
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| 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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| Dilip Roy Chowdhury : Colleagues
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| Mridula Chatterjee : Colleagues
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| R. K. Samanta : Colleagues
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