An Ensemble Deep Dynamic Algorithm (EDDA) to Predict the Heart Disease

Authors

  • J. Nageswara Rao  Research Scholar Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
  • Dr. R. Satya Prasad  Professor, Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET218118

Keywords:

Deep Learning, ML, EDDA Ensemble Deep Dynamic Algorithm, Deep Boltzmann Machine.

Abstract

Nowadays heart disease becomes more complicated to every human being. Machine Learning and Deep Learning plays the major role in processing the automatic systems. Prediction of heart disease is most difficult task because many algorithms perform limited operations. The aim of the paper is to increase the accuracy and prediction values. Various heart disease datasets are available for the research. Deep Learning (DL) algorithms play the major role in prediction of heart disease. Prediction can be done in the early stages to reduce the risk of death for the humans. In this paper, An Ensemble Deep Dynamic Algorithm (EDDA) is introduced to increase the accuracy of prediction values. The EDDA follows the some steps to process the prediction of heart disease. The steps are as follows: Linear Regression and Deep Boltzmann Machine (DBM) is applied on the selected dataset. Performance is calculated in terms of sensitivity, specificity and accuracy are shown with the comparative results.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-01-30

Issue

Volume 8, Issue 1, January-February-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
J. Nageswara Rao, Dr. R. Satya Prasad "An Ensemble Deep Dynamic Algorithm (EDDA) to Predict the Heart Disease" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 1, pp.105-111, January-February-2021. Available at doi : https://doi.org/10.32628/IJSRSET218118