Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction-Reference-Cited by-同舟云学术

Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction

Published:2023-04-30 Issue:2 Volume:11 Page:253-261
ISSN:2347-470X
Container-title:International Journal of Electrical and Electronics Research
language:en
Short-container-title:IJEER

Author:

Krishnan Dr. V. Gokula¹,Saradhi Dr. M. V. Vijaya²,Kumar Dr. S. Sai³,Dhanalakshmi G.⁴,Pushpa P.⁵,Vijayaraja Dr. V.⁶

Affiliation:

1. Professor, Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India

2. Professor and Head, Department of CSE, ACE Engineering College, Ghatkesar, Hyderabad, Telangana, India

3. Assistant Professor, Department of IT, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

4. Associate Professor, Department of IT, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India

5. Assistant Professor, Department of AIDS, Rajalakshmi Institute of Technology, Kuthambakkam, Chennai, Tamil Nadu, India

6. Research Scholar, Department of AIDS, R M K College of Engineering and Technology, Kavaraipettai, Tamil Nadu, India

Abstract

The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures.

Publisher

FOREX Publication

Subject

Electrical and Electronic Engineering,Engineering (miscellaneous)

Reference32 articles.

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