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

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: Weng L.Y., Omar J.B., Siah Y.K., Abidin I.B.Z., Ahmed S.K.
Other Authors: 26326032700
Format: Conference paper
Published: 2023
Subjects:
Artificial intelligenc
Diabetes
Kohonen Self Organizing Maps
Neural networks
Pima Indians
Artificial intelligence
Backpropagation
Data flow analysis
Measurements
Strength of materials
2-group
Artificial Neural Network
Cross validation
Data sets
Kohonen self-organizing maps
Self organizing maps
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Institution: Universiti Tenaga Nasional
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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%. � 2009 IEEE.

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