Comparison of Back Propagation Algorithms and Fusion Methodology Using Dempster-Shafer Rule in Medical Application

Authors(1) :-B. Sumathi

This paper presents various Back Propagation algorithms for the diagnosis of hypertension. Parameters such as learning rate and momentum coefficients are used to improve the rate of convergence and controls the feedback loop of Back Propagation algorithm. The value for learning rate and momentum factors are varied instead of using fixed value to make the learning more effectively during the training process. The primary classifiers used in this paper are Quasi-Newton (QN), Gradient Descent (GD) and Levenberg-Marquardt (LM) Back Propagation training algorithms, each using different learning function. The Dempster-Shafer's rule has been adopted to combine the output of these three Back Propagation neural networks into single one to enhance the target result. The experimental result shows that the fusion method would provide a significantly higher accuracy for the diagnosis of hypertension.

Authors and Affiliations

B. Sumathi
Department of Computer Science, CMS College of Science and Commerce, Coimbatore, Tamil Nadu, India

Back Propagation, Dempster-Shafer, Accuracy, Learning Rate, Momentum

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 165-171
Manuscript Number : CSEIT183727
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

B. Sumathi, "Comparison of Back Propagation Algorithms and Fusion Methodology Using Dempster-Shafer Rule in Medical Application", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.165-171, September-October-2018.
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