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233811

Covid-19 Patients Diagnosis (CPD) Strategy Using Data Mining Techniques

Article

Last updated: 04 Jan 2025

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Subjects

-

Tags

Electronics and Communications Engineering

Abstract

Covid-19, the world continues to live in anxiety and instability despite efforts to find a vaccine and emerge from this crisis.  Especially after the emergence of a new mutated Corona virus called Omicron. This mutated sparked a state of controversy about the extent of its impact and its ability to spread among people. The Covid-19 epidemic has thrown the world economy into disarray. It also resulted in a widespread suspension of work and output throughout society, hurting economic and society. This paper introduces a Covid-19 Patients Diagnosis (CPD) strategy that works to find a fast and highly effective prognosis for diagnosing Covid-19 patients. The proposed strategy has two main stages named Feature Selection Stage (FSS) and Covid-19 Diagnosis Stage (CDS). The FSS has main objective to select the powerful features for the diagnosis stage. The features are selected in the FSS by using Chi-Square Feature Selection (CSFS) method. In fact, CSFS is a filter feature selection technique that has the ability to quickly choose the most effective subset of features. Then, quick and accurate diagnosis is provided by using Improved K-Nearest Neighbors (IKNN). The main idea in the proposed IKNN is that a circle with a radius value that equals the average distance of K of the closet items will be constructed and then the nearest M of items will be determined to classify the patient to the correct class “Covid" or “Non-Covid".  The results explain that the proposed strategy called CPD gives an accuracy of up to 96.36%.

DOI

10.21608/bfemu.2022.233811

Authors

First Name

Alaa

Last Name

Mohamed

MiddleName

Mostafa

Affiliation

Master Degree Researcher of Electronics and Communication Department, Faculty of Engineering, Mansoura University works at Delta Higher Institute for Engineering and Technology

Email

alaa.mostafa545454@gmail.com

City

Egypt

Orcid

-

First Name

Ahmed

Last Name

Saleh

MiddleName

-

Affiliation

Professor at the Computers and Control Department, Faculty of Engineering, Mansoura University, Egypt.

Email

aisaleh@yahoo.com

City

Mansoura

Orcid

-

First Name

Doaa

Last Name

A. Altantawy

MiddleName

-

Affiliation

Assistant Professor at the Electronics and Communication Department, Faculty of Engineering, Mansoura University, Egypt

Email

doaa1adel@mans.edu.eg

City

-

Orcid

0000-0001-6476-2934

First Name

Mohy Eldin

Last Name

Abo-Elsoud

MiddleName

Ahmed

Affiliation

Professor at the Electronics and Communication Department, Faculty of Engineering, Mansoura University, Egypt

Email

mohyldin@gmail.com

City

-

Orcid

-

Volume

47

Article Issue

2

Related Issue

32041

Issue Date

2022-04-01

Receive Date

2022-01-09

Publish Date

2022-04-27

Page Start

32

Page End

41

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 47_Issue 2_Pages 32-41.pdf

PDF . 1.8MB

Link

https://bfemu.journals.ekb.eg/article_233811.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=233811

Order

12

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

Covid-19 Patients Diagnosis (CPD) Strategy Using Data Mining Techniques

Details

Type

Article

Created At

22 Jan 2023