Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction

Artificial Intelligence plays a vital role in developing machines or software that can create intelligence. Artificial Neural Networks is a field of neuroscience which contributes tremendous developments in Artificial Intelligence. This paper focuses on the study of performance of various training a...

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Bibliographic Details
Published in2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) pp. 201 - 206
Main Authors Saji, Sumi Alice, Balachandran, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2015
Subjects
Artificial Neural Network
Artificial neural networks
Backpropagation
Diabetes
Diabetes Mellitus
Levenberg-Marquardt
Multi Layer Perceptrons
Multilayer perceptrons
Prediction algorithms
Training
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DOI10.1109/ICACEA.2015.7164695

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Summary:Artificial Intelligence plays a vital role in developing machines or software that can create intelligence. Artificial Neural Networks is a field of neuroscience which contributes tremendous developments in Artificial Intelligence. This paper focuses on the study of performance of various training algorithms of Multilayer Perceptrons in Diabetes Prediction. In this study, we have used Pima Indian Diabetes data set from UCI Machine Learning Repository as input dataset. The system is implemented in MatlabR2013. The Pima Indian Diabetes dataset consists of about 768 instances. The input data is the patient history and the target output is the prediction result as tested positive or tested negative. From the performance analysis, it was observed that out of all the training algorithms, Levenberg-Marquardt Algorithm has given optimal training results.
DOI:10.1109/ICACEA.2015.7164695