Publication:
Improvement of ANN-BP by data pre-segregation using SOM

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dc.citedby7
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorOmar J.B.en_US
dc.contributor.authorSiah Y.K.en_US
dc.contributor.authorAbidin I.B.Z.en_US
dc.contributor.authorAhmed S.K.en_US
dc.contributor.authorid26326032700en_US
dc.contributor.authorid24463418200en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid25926812900en_US
dc.date.accessioned2023-12-28T07:30:46Z
dc.date.available2023-12-28T07:30:46Z
dc.date.issued2009
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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5069941
dc.identifier.doi10.1109/CIMSA.2009.5069941
dc.identifier.epage178
dc.identifier.scopus2-s2.0-77950851517
dc.identifier.spage175
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77950851517&doi=10.1109%2fCIMSA.2009.5069941&partnerID=40&md5=b72ae9d5978d4c50b73b905334598f96
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29684
dc.pagecount3
dc.sourceScopus
dc.sourcetitle2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009
dc.subjectArtificial intelligenc
dc.subjectDiabetes
dc.subjectKohonen Self Organizing Maps
dc.subjectNeural networks
dc.subjectPima Indians
dc.subjectArtificial intelligence
dc.subjectBackpropagation
dc.subjectData flow analysis
dc.subjectMeasurements
dc.subjectStrength of materials
dc.subject2-group
dc.subjectArtificial intelligenc
dc.subjectArtificial Neural Network
dc.subjectCross validation
dc.subjectData sets
dc.subjectKohonen self-organizing maps
dc.subjectSelf organizing maps
dc.titleImprovement of ANN-BP by data pre-segregation using SOMen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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