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CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications
Today, there is a level of panic and chaos dominating the entire world due to the massive outbreak in the second wave of COVID-19 disease. As the disease has numerous symptoms ranging from a simple fever to the inability to breathe, which may lead to death. One of these symptoms is a cough which is...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803577/ https://www.ncbi.nlm.nih.gov/pubmed/35126765 http://dx.doi.org/10.1007/s12652-022-03732-0 |
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author | Hemdan, Ezz El-Din El-Shafai, Walid Sayed, Amged |
author_facet | Hemdan, Ezz El-Din El-Shafai, Walid Sayed, Amged |
author_sort | Hemdan, Ezz El-Din |
collection | PubMed |
description | Today, there is a level of panic and chaos dominating the entire world due to the massive outbreak in the second wave of COVID-19 disease. As the disease has numerous symptoms ranging from a simple fever to the inability to breathe, which may lead to death. One of these symptoms is a cough which is considered one of the most common symptoms for COVID-19 disease. Recent research shows that the cough of a COVID-19 patient has distinct features that are different from other diseases. Consequently, the cough sound can be detected and classified to be used as a preliminary diagnosis of the COVID-19, which will help in reducing the spreading of that disease. The artificial intelligence (AI) engine can diagnose COVID-19 diseases by executing differential analysis of its inherent characteristics and comparing it to other non-COVID-19 coughs. However, the diagnosis of a COVID-19 infection by cough alone is an extremely challenging multidisciplinary problem. Therefore, this paper proposes a hybrid framework for efficiently COVID-19 detection and diagnosis using various ML algorithms from cough audio signals. The accuracy of this framework is improved with the utilization of the genetic algorithm with the ML techniques. We also assess the proposed system called CR19 for diagnosis on metrics such as precision, recall, F-measure. The results proved that the hybrid (GA-ML) technique provides superior results based on different evaluation metrics compared with ML approaches such as LR, LDA, KNN, CART, NB, and SVM. The proposed framework achieve an accuracy equal to 92.19%, 94.32%, 97.87%, 92.19%, 91.48%, and 93.61% in compared with the ML are 90.78, 92.90, 95.74, 87.94, 81.56, and 92.198 for LR, LDA, KNN, CART, NB, and SVM respectively. The proposed framework will efficiently help the physicians provide a proper medical decision regarding the COVID-19 analysis, thereby saving more lives. Therefore, this CR19 framework can be a clinical decision assistance tool used to channel clinical testing and treatment to those who need it the most, thereby saving more lives. |
format | Online Article Text |
id | pubmed-8803577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-88035772022-02-01 CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications Hemdan, Ezz El-Din El-Shafai, Walid Sayed, Amged J Ambient Intell Humaniz Comput Original Research Today, there is a level of panic and chaos dominating the entire world due to the massive outbreak in the second wave of COVID-19 disease. As the disease has numerous symptoms ranging from a simple fever to the inability to breathe, which may lead to death. One of these symptoms is a cough which is considered one of the most common symptoms for COVID-19 disease. Recent research shows that the cough of a COVID-19 patient has distinct features that are different from other diseases. Consequently, the cough sound can be detected and classified to be used as a preliminary diagnosis of the COVID-19, which will help in reducing the spreading of that disease. The artificial intelligence (AI) engine can diagnose COVID-19 diseases by executing differential analysis of its inherent characteristics and comparing it to other non-COVID-19 coughs. However, the diagnosis of a COVID-19 infection by cough alone is an extremely challenging multidisciplinary problem. Therefore, this paper proposes a hybrid framework for efficiently COVID-19 detection and diagnosis using various ML algorithms from cough audio signals. The accuracy of this framework is improved with the utilization of the genetic algorithm with the ML techniques. We also assess the proposed system called CR19 for diagnosis on metrics such as precision, recall, F-measure. The results proved that the hybrid (GA-ML) technique provides superior results based on different evaluation metrics compared with ML approaches such as LR, LDA, KNN, CART, NB, and SVM. The proposed framework achieve an accuracy equal to 92.19%, 94.32%, 97.87%, 92.19%, 91.48%, and 93.61% in compared with the ML are 90.78, 92.90, 95.74, 87.94, 81.56, and 92.198 for LR, LDA, KNN, CART, NB, and SVM respectively. The proposed framework will efficiently help the physicians provide a proper medical decision regarding the COVID-19 analysis, thereby saving more lives. Therefore, this CR19 framework can be a clinical decision assistance tool used to channel clinical testing and treatment to those who need it the most, thereby saving more lives. Springer Berlin Heidelberg 2022-02-01 /pmc/articles/PMC8803577/ /pubmed/35126765 http://dx.doi.org/10.1007/s12652-022-03732-0 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Hemdan, Ezz El-Din El-Shafai, Walid Sayed, Amged CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
title | CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
title_full | CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
title_fullStr | CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
title_full_unstemmed | CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
title_short | CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
title_sort | cr19: a framework for preliminary detection of covid-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803577/ https://www.ncbi.nlm.nih.gov/pubmed/35126765 http://dx.doi.org/10.1007/s12652-022-03732-0 |
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