Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/6301
Title: Improvement of ANN-BP by data pre-segregation using SOM
Authors: Weng, L.Y. 
Omar, J.B. 
Siah, Y.K. 
Abidin, I.B.Z. 
Ahmed, S.K. 
Issue Date: 2009
Abstract: Artificial intelligence is used to predict the onset of diabetes based on data measured from Pima Indians. This research is comparing the results gained from using same artificial neural networks-back propagation (ANN-BP) engine for 2 differently prepared data. The first data set consists of the entire data set which is cross validated, while the second dataset is segregated into 2 groups using Kohonen Self Organizing Maps (SOM) which are then cross validated. Splitting the files prior to implementing the cross validation improves the general accuracy of the ANN-BP whereby the positively predicted diabetes cases percentage increased from 72% to 99%. Meanwhile the prediction of the negative diabetic cases percentage increased from 80% to 97%. © 2009 IEEE.
URI: http://dspace.uniten.edu.my/jspui/handle/123456789/6301
Appears in Collections:COE Scholarly Publication

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