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Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network

  • J. VijayashreeEmail author
  • H. Parveen Sultana
Original Paper
  • 8 Downloads
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health

Abstract

According to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner with minimum time. So, hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart disease in earlier stage. First, heart disease data has been collected from UCI repository, dimensionality of the data is minimized by hybridized Ruzzo–Tompa memetic approach. After reducing the number of features, that are trained by deep learning approach which analyze the features using maximum number of hidden layers that used to predict heart disease features successfully while making the Neocognitron neural network classification. Further efficiency of the system is evaluated using MATLAB based simulation results.

Keywords

Heart disease Hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network Heart disease data set-UCI repository 

Notes

Compliance with ethical standards

Conflict of interest

J.Vijayashree and Parveen Sultana.H 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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