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|Title:||Improvement of ANN-BP by data pre-segregation using SOM||Authors:||Weng, L.Y.
|Issue Date:||2009||Journal:||2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009 2009, Article number 5069941, Pages 175-178||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||DOI:||10.1109/CIMSA.2009.5069941|
|Appears in Collections:||COE Scholarly Publication|
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