Please use this identifier to cite or link to this item:
|Title:||Improvement of ANN-BP by data pre-segregation using SOM||Authors:||Weng, L.Y.
|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|
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.