Enhanced Backpropagation Approach for Identifying Genetic Disease

Abstract:

Classification of data on genetic disease is a useful application in microarray analysis. The genetic disease data analysis has the potential for discovering the diseased genes which may be the signature of certain diseases. Machine learning methodologies and data mining techniques are used to predict genetic disease associations of bio informatics data. Among numerous existing methods for gene selection, Backpropagation algorithm has become one of the leading methods and it gives less classification accuracy. It aims to develop a new classification algorithm (Enhanced Backpropagation Algorithm) for genetic disease analysis. Knowledge derived by the Enhanced Backpropagation Algorithm has high classification accuracy with the ability to identify the most significant genes.

• Periodical: Applied Mechanics and Materials (Volume 622)
• Pages: 75-80
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.622.75
• Online since: August 2014
• Authors: Baskar Nisha*, B. Madasamy, J.Jebamalar Tamilselvi
• Keywords: Backpropagation Algorithm, Classification Accuracy, Enhanced Backpropagation Algorithm, Genetic Disease

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