Whale optimization algorithm for Covid-19 detection based on ECG

Imene Latreche; Mohamed Akram Khelili; Sihem Slatnia; Okba Kazar; Saad Harous

doi:10.1016/B978-0-32-395365-8.00046-4

Whale optimization algorithm for Covid-19 detection based on ECG

Imene Latreche, Mohamed Akram Khelili, Sihem Slatnia, Okba Kazar, Saad Harous

Department of Computer Science and Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

COVID-19 has been declared a global catastrophe in recent years, resulting in several fatalities. The bulk of COVID-19 detection investigations use on X-ray pictures and CT scans of the lungs. Recent clinical research, however, indicated that COVID-19 had an effect on the cardiac signal. As a result, we used the Whale Optimization Algorithm to explore the ECG signal for COVID-19 identification. The model was configured using the WOA used for hyperparameter auto-selection. We used both binary and three-class classification algorithms in the suggested methodology. The created model obtained 99% accuracy for binary classification and 83% accuracy for three-class classification.

Original language	English
Title of host publication	Handbook of Whale Optimization Algorithm
Subtitle of host publication	Variants, Hybrids, Improvements, and Applications
Publisher	Elsevier
Pages	567-579
Number of pages	13
ISBN (Electronic)	9780323953658
ISBN (Print)	9780323953641
DOIs	https://doi.org/10.1016/B978-0-32-395365-8.00046-4
Publication status	Published - Jan 1 2023

Keywords

Covid-19 detection
Deep learning
ECG images
Metaheuristic
Whale optimization algorithm

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1016/B978-0-32-395365-8.00046-4

Cite this

@inbook{7b4b2aeccfdd471cb1cdd90725331044,

title = "Whale optimization algorithm for Covid-19 detection based on ECG",

abstract = "COVID-19 has been declared a global catastrophe in recent years, resulting in several fatalities. The bulk of COVID-19 detection investigations use on X-ray pictures and CT scans of the lungs. Recent clinical research, however, indicated that COVID-19 had an effect on the cardiac signal. As a result, we used the Whale Optimization Algorithm to explore the ECG signal for COVID-19 identification. The model was configured using the WOA used for hyperparameter auto-selection. We used both binary and three-class classification algorithms in the suggested methodology. The created model obtained 99% accuracy for binary classification and 83% accuracy for three-class classification.",

keywords = "Covid-19 detection, Deep learning, ECG images, Metaheuristic, Whale optimization algorithm",

author = "Imene Latreche and Khelili, {Mohamed Akram} and Sihem Slatnia and Okba Kazar and Saad Harous",

year = "2023",

month = jan,

day = "1",

doi = "10.1016/B978-0-32-395365-8.00046-4",

language = "English",

isbn = "9780323953641",

pages = "567--579",

booktitle = "Handbook of Whale Optimization Algorithm",

publisher = "Elsevier",

}

TY - CHAP

T1 - Whale optimization algorithm for Covid-19 detection based on ECG

AU - Latreche, Imene

AU - Khelili, Mohamed Akram

AU - Slatnia, Sihem

AU - Kazar, Okba

AU - Harous, Saad

PY - 2023/1/1

Y1 - 2023/1/1

N2 - COVID-19 has been declared a global catastrophe in recent years, resulting in several fatalities. The bulk of COVID-19 detection investigations use on X-ray pictures and CT scans of the lungs. Recent clinical research, however, indicated that COVID-19 had an effect on the cardiac signal. As a result, we used the Whale Optimization Algorithm to explore the ECG signal for COVID-19 identification. The model was configured using the WOA used for hyperparameter auto-selection. We used both binary and three-class classification algorithms in the suggested methodology. The created model obtained 99% accuracy for binary classification and 83% accuracy for three-class classification.

AB - COVID-19 has been declared a global catastrophe in recent years, resulting in several fatalities. The bulk of COVID-19 detection investigations use on X-ray pictures and CT scans of the lungs. Recent clinical research, however, indicated that COVID-19 had an effect on the cardiac signal. As a result, we used the Whale Optimization Algorithm to explore the ECG signal for COVID-19 identification. The model was configured using the WOA used for hyperparameter auto-selection. We used both binary and three-class classification algorithms in the suggested methodology. The created model obtained 99% accuracy for binary classification and 83% accuracy for three-class classification.

KW - Covid-19 detection

KW - Deep learning

KW - ECG images

KW - Metaheuristic

KW - Whale optimization algorithm

UR - http://www.scopus.com/inward/record.url?scp=85189599526&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85189599526&partnerID=8YFLogxK

U2 - 10.1016/B978-0-32-395365-8.00046-4

DO - 10.1016/B978-0-32-395365-8.00046-4

M3 - Chapter

AN - SCOPUS:85189599526

SN - 9780323953641

SP - 567

EP - 579

BT - Handbook of Whale Optimization Algorithm

PB - Elsevier

ER -

Whale optimization algorithm for Covid-19 detection based on ECG

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this