Heart Disease Prediction Based on an Optimal Feature Selection Method using Autoencoder

Authors

  • Azhar M. A.  PG Scholar, Department of Computer Science and Engineering, Government Engineering College, Idukki, Kerala, India
  • Princy Ann Thomas  Associate Professor, Department of Computer Science and Engineering, Government Engineering College, Idukki, India

DOI:

https://doi.org/10.32628/IJSRST20748

Keywords:

Data Mining, Autoencoder, Hybrid, Classification Model, Dynamic Integration Algorithm

Abstract

Heart Failure is one of the common diseases that can lead to dangerous situations. There are several data available within the healthcare systems. However, there was an absence of successful analysis methods to find connections and patterns in health care data. Some Machine learning methods can help us remedy this circumstance. This helps in getting a better insight into the concept of a classification problem. In many classification problems, it is difficult to learn good classifiers before removing these unwanted features due to the huge size of the data. In my work, we have used an artificial neural network-based autoencoder for effective feature selection The aim of feature selection is improving prediction performance and providing a better understanding of the process data. Hybrid Classification method with a dynamic integration algorithm for classification that aims at finding optimal features by applying machine learning techniques resulting in improving the performance in the prediction of cardiovascular disease.

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Webliography

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International Journal of Scientific Research in Science and Technology

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Published

2020-08-30

Issue

Volume 7, Issue 4, July-August-2020

Section

Research Articles

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How to Cite

[1]
Azhar M. A., Princy Ann Thomas "Heart Disease Prediction Based on an Optimal Feature Selection Method using Autoencoder" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 7, Issue 4, pp.25-38, July-August-2020. Available at doi : https://doi.org/10.32628/IJSRST20748