Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/11423
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dc.contributor.authorWeng, L.Y.
dc.contributor.authorOmar, J.B.
dc.contributor.authorSiah, Y.K.
dc.contributor.authorAbidin, I.B.Z.
dc.contributor.authorAhmed, S.K.
dc.date.accessioned2019-01-02T06:41:08Z-
dc.date.available2019-01-02T06:41:08Z-
dc.date.issued2009
dc.identifier.urihttp://dspace.uniten.edu.my/jspui/handle/123456789/11423-
dc.description.abstractArtificial 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.
dc.titleImprovement of ANN-BP by data pre-segregation using SOM
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.deptUniversiti Tenaga Nasional-
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