Heart disease prediction using convolutional neural network

Ajay Sharma, Tarun Pal, Varun Jaiswal

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

13 Scopus citations

Abstract

The computational method presented in this chapter is based on deep learning and uses a convolutional neural network (CNN). Using CNN, we can develop an appropriate methodology and implement those methods to develop a prediction-based method that can provide pertinent and valuable information and act as a helping body for researchers and radiologists in diagnosis, treatment, and prevention of heart disease. This chapter is anticipated to help researchers, students, and practitioners related to medical diagnosis of computer science in how to implement deep learning-based methods for disease prediction. It gives the idea of CNN, which involves a deep learning approach, especially used in image processing and understanding or classification of images. The developed model classifies heart disease-related medical images with the help of CNN with an accuracy of ~96%. This model is developed in TensorFlow, a Google Python-based library used for the deep learning-based tasks.

Original languageEnglish
Title of host publicationCardiovascular and Coronary Artery Imaging
Subtitle of host publicationVolume 1
PublisherElsevier
Pages245-272
Number of pages28
ISBN (Electronic)9780128227060
ISBN (Print)9780128227077
DOIs
StatePublished - 1 Jan 2021
Externally publishedYes

Keywords

  • CNN
  • deep learning
  • heart disease
  • image processing

ASJC Scopus subject areas

  • General Computer Science

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