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Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network
COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of peopl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659585/ https://www.ncbi.nlm.nih.gov/pubmed/33204229 http://dx.doi.org/10.1016/j.asoc.2020.106906 |
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author | Shaban, Warda M. Rabie, Asmaa H. Saleh, Ahmed I. Abo-Elsoud, M.A. |
author_facet | Shaban, Warda M. Rabie, Asmaa H. Saleh, Ahmed I. Abo-Elsoud, M.A. |
author_sort | Shaban, Warda M. |
collection | PubMed |
description | COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test. |
format | Online Article Text |
id | pubmed-7659585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76595852020-11-13 Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network Shaban, Warda M. Rabie, Asmaa H. Saleh, Ahmed I. Abo-Elsoud, M.A. Appl Soft Comput Article COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test. Elsevier B.V. 2021-02 2020-11-12 /pmc/articles/PMC7659585/ /pubmed/33204229 http://dx.doi.org/10.1016/j.asoc.2020.106906 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Shaban, Warda M. Rabie, Asmaa H. Saleh, Ahmed I. Abo-Elsoud, M.A. Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |
title | Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |
title_full | Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |
title_fullStr | Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |
title_full_unstemmed | Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |
title_short | Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |
title_sort | detecting covid-19 patients based on fuzzy inference engine and deep neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659585/ https://www.ncbi.nlm.nih.gov/pubmed/33204229 http://dx.doi.org/10.1016/j.asoc.2020.106906 |
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