Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction

Title

Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction

Creator

Saji S.A.; Balachandran K.

Description

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. 2015 IEEE.

Source

Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015, pp. 201-206.

Date

2015-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Neural Network; Diabetes Mellitus; Levenberg-Marquardt; Multi Layer Perceptrons

Coverage

Saji S.A., Department of Computer Science and Engineering, Christ University, Bangalore, India; Balachandran K., Department of Computer Science and Engineering, Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-146736911-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICACEA.2015.7164695

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947997075&doi=10.1109%2fICACEA.2015.7164695&partnerID=40&md5=be764da9d2205eef644ebec6344e717a