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Hybrid swarm intelligent redundancy relevance (RR) with convolution trained compositional pattern neural network expert system for diagnosis of diabetes

  • J. JayashreeEmail author
  • S. Ananda Kumar
Original Paper
  • 8 Downloads
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health

Abstract

According to the World Health Organization (WHO) report, 438 million peoples are affected by diabetics in the upcoming year of 2030. Due to the seriousness of the diabetics’ disease, it has to be predicted in the earlier stage but the minimum symptoms of diabetic failure to predict in earlier stage. So, the automatic and earlier diabetic prediction system needs to be created for eliminating the serious factors in medical field. There are several earlier diabetic prediction system is created with the help of hybridized machine learning techniques but they are difficult to process the huge dimension of data as well as consume more time for predicting diabetic related features. For overcoming the above issues, in this paper introduces the swarm intelligent redundancy relevance (RR) along with convolution trained compositional pattern neural network for predicting the diabetic disease. Initially, the diabetic data has been collected from Pima Indian Diabetic dataset, the dimensionality of data is reduced by swarm intelligence RR techniques, the selected features are trained by layers of convolution networks that helps to speed up the diabetic prediction process. Finally, diabetic classification process is done by compositional pattern neural network and the efficiency is evaluated using MATLAB based experimental results.

Keywords

Diabetics Evolutionary swarm intelligent redundancy relevance (RR) along with convolution trained compositional pattern neural network Pima Indian diabetic dataset 

Notes

Compliance with ethical standards

Conflict of interest

J. Jayashree and Ananda Kumar. S declare that they have no conflict of interest.

Ethical approval

Not Applicable

Informed consent

Not Applicable

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Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia

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