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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Bibliographic Details
Main Authors: Leong Yeng Weng, Bin Omar, J., Siah, Y.K., Abidin, I., Ahmed, S.K.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial intelligenc
Artificial intelligence
Artificial neural networks
Biological neural networks
Diabetes
Insulin
Kohonen Self Organizing Maps
Multi-layer neural network
Neural networks
Neurons
Pima Indians
Self organizing feature maps
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Summary: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%.
ISSN:2159-1547
2159-1555
DOI:10.1109/CIMSA.2009.5069941