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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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%.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009
Pages175-178
Number of pages4
DOIs
Publication statusPublished - 01 Dec 2009
Event2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009 - Hong Kong, China
Duration: 11 May 200913 May 2009

Publication series

Name2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009

Other

Other2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009
CountryChina
CityHong Kong
Period11/05/0913/05/09

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Leong, Y. W., Omar, J., Yap, K. S., Zainal Abidin, I., & Khaleel Ahmed, S. (2009). Improvement of ANN-BP by data pre-segregation using SOM. In 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009 (pp. 175-178). [5069941] (2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009). https://doi.org/10.1109/CIMSA.2009.5069941