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

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 ent...

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Main Authors: Weng, L.Y., Omar, J.B., Siah, Y.K., Abidin, I.B.Z., Ahmed, S.K.
Format: Conference Paper
Language:English
Published: 2017
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/6301
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spelling my.uniten.dspace-50322018-12-07T08:09:49Z Improvement of ANN-BP by data pre-segregation using SOM Weng, L.Y. Omar, J.B. Siah, Y.K. Abidin, I.B.Z. Ahmed, S.K. 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. 2017-11-14T03:21:31Z 2017-11-14T03:21:31Z 2009 Conference Paper http://dspace.uniten.edu.my/jspui/handle/123456789/6301 10.1109/CIMSA.2009.5069941 en 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009 2009, Article number 5069941, Pages 175-178
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description 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.
format Conference Paper
author Weng, L.Y.
Omar, J.B.
Siah, Y.K.
Abidin, I.B.Z.
Ahmed, S.K.
spellingShingle Weng, L.Y.
Omar, J.B.
Siah, Y.K.
Abidin, I.B.Z.
Ahmed, S.K.
Improvement of ANN-BP by data pre-segregation using SOM
author_facet Weng, L.Y.
Omar, J.B.
Siah, Y.K.
Abidin, I.B.Z.
Ahmed, S.K.
author_sort Weng, L.Y.
title Improvement of ANN-BP by data pre-segregation using SOM
title_short Improvement of ANN-BP by data pre-segregation using SOM
title_full Improvement of ANN-BP by data pre-segregation using SOM
title_fullStr Improvement of ANN-BP by data pre-segregation using SOM
title_full_unstemmed Improvement of ANN-BP by data pre-segregation using SOM
title_sort improvement of ann-bp by data pre-segregation using som
publishDate 2017
url http://dspace.uniten.edu.my/jspui/handle/123456789/6301
_version_ 1644493595502182400
score 13.226497

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