Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)

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A Novel Framework for Grading of Heart Attack

Authors
T. M. Rajesh1, *, M. N. RenukaDevi1, S. G. Shaila1, CauveryRaju1
1Department of Computer Science and Engineering, Dayananda Sagar University, Bangalore, India
*Corresponding author. Email: rajesh-cse@dsu.edu.in
Corresponding Author
T. M. Rajesh
Available Online 1 May 2023.
DOI
10.2991/978-94-6463-136-4_28How to use a DOI?
Keywords
Fast Fourier Transform; Discrete Fourier Transform; Decision Tree; Principal Component Analysis
Abstract

In recent years, cardiovascular diseases have become common. Serious health problems arise in the human body as a result of an unhealthy lifestyle, the use of alcohol and tobacco, obesity, stress, and dietary changes. This has complicated surgeons’ ability to diagnose heart failure at the right time. A heart attack occurs when the blood flow that brings oxygen to the heart muscle is severely condensed or cut off completely. ECG is a medical test that is used in the detection of heart attacks in patients. Extracting the essential features from ECG images is the most crucial task. The key features are extracted using connected component analysis, hierarchical centroid, Hough line transform, and height and width. Various techniques like Fast Fourier Transform, Discrete Fourier Transform, Decision Tree and Principal Component Analysis are used to predict heart failure. In this model, we are going to examine ECG signal images and detect whether the person is prone to heart attack or not. A comparative study of different models showed that the proposed work enhanced the previous accuracy score in predicting heart failure using FFT.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
Series
Advances in Computer Science Research
Publication Date
1 May 2023
ISBN
10.2991/978-94-6463-136-4_28
ISSN
2352-538X
DOI
10.2991/978-94-6463-136-4_28How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - T. M. Rajesh
AU  - M. N. RenukaDevi
AU  - S. G. Shaila
AU  - CauveryRaju
PY  - 2023
DA  - 2023/05/01
TI  - A Novel Framework for Grading of Heart Attack
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 320
EP  - 339
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_28
DO  - 10.2991/978-94-6463-136-4_28
ID  - Rajesh2023
ER  -