Staff View: Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data

Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an eﬀort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more diﬃcult for a specialis...

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Main Authors:	Ahmed, M. Dinar, A. Raheem, Enas, Abdulkareem, Karrar Hameed, Abed Mohammed, Mazin, Oleiwie, Marwan Ghazi, Zayr, Fawzi Hasan, Al-Boridi, Omar, Mohammed Al-Andoli, Mohammed Nasser, Ahmed Al-Mhiqani, Mohammed Nasser
Format:	Article
Language:	English
Published:	Hindawi Limited 2022
Online Access:	http://eprints.utem.edu.my/id/eprint/27779/2/0272912082023317.pdf http://eprints.utem.edu.my/id/eprint/27779/ https://onlinelibrary.wiley.com/doi/10.1155/2022/7675925 https://doi.org/10.1155/2022/7675925
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spelling	my.utem.eprints.277792024-10-09T09:56:02Z http://eprints.utem.edu.my/id/eprint/27779/ Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data Ahmed, M. Dinar A. Raheem, Enas Abdulkareem, Karrar Hameed Abed Mohammed, Mazin Oleiwie, Marwan Ghazi Zayr, Fawzi Hasan Al-Boridi, Omar Mohammed Al-Andoli, Mohammed Nasser Ahmed Al-Mhiqani, Mohammed Nasser The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an eﬀort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more diﬃcult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO 2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19patients were recruited from the Azizi a primary health care sector/Wasit Health Directorate/Ministry of Health to form diﬀerent combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of82%. The established prediction system allows for the early identiﬁcation of three severity cases, which reduces deaths. Hindawi Limited 2022 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27779/2/0272912082023317.pdf Ahmed, M. Dinar and A. Raheem, Enas and Abdulkareem, Karrar Hameed and Abed Mohammed, Mazin and Oleiwie, Marwan Ghazi and Zayr, Fawzi Hasan and Al-Boridi, Omar and Mohammed Al-Andoli, Mohammed Nasser and Ahmed Al-Mhiqani, Mohammed Nasser (2022) Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data. Mobile Information Systems, 2022 (675925). pp. 1-8. ISSN 1574-017X https://onlinelibrary.wiley.com/doi/10.1155/2022/7675925 https://doi.org/10.1155/2022/7675925
institution	Universiti Teknikal Malaysia Melaka
building	UTEM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknikal Malaysia Melaka
content_source	UTEM Institutional Repository
url_provider	http://eprints.utem.edu.my/
language	English
description	The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an eﬀort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more diﬃcult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO 2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19patients were recruited from the Azizi a primary health care sector/Wasit Health Directorate/Ministry of Health to form diﬀerent combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of82%. The established prediction system allows for the early identiﬁcation of three severity cases, which reduces deaths.
format	Article
author	Ahmed, M. Dinar A. Raheem, Enas Abdulkareem, Karrar Hameed Abed Mohammed, Mazin Oleiwie, Marwan Ghazi Zayr, Fawzi Hasan Al-Boridi, Omar Mohammed Al-Andoli, Mohammed Nasser Ahmed Al-Mhiqani, Mohammed Nasser
spellingShingle	Ahmed, M. Dinar A. Raheem, Enas Abdulkareem, Karrar Hameed Abed Mohammed, Mazin Oleiwie, Marwan Ghazi Zayr, Fawzi Hasan Al-Boridi, Omar Mohammed Al-Andoli, Mohammed Nasser Ahmed Al-Mhiqani, Mohammed Nasser Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data
author_facet	Ahmed, M. Dinar A. Raheem, Enas Abdulkareem, Karrar Hameed Abed Mohammed, Mazin Oleiwie, Marwan Ghazi Zayr, Fawzi Hasan Al-Boridi, Omar Mohammed Al-Andoli, Mohammed Nasser Ahmed Al-Mhiqani, Mohammed Nasser
author_sort	Ahmed, M. Dinar
title	Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data
title_short	Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data
title_full	Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data
title_fullStr	Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data
title_full_unstemmed	Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data
title_sort	towards automated multiclass severity prediction approach for covid-19 infections based on combinations of clinical data
publisher	Hindawi Limited
publishDate	2022
url	http://eprints.utem.edu.my/id/eprint/27779/2/0272912082023317.pdf http://eprints.utem.edu.my/id/eprint/27779/ https://onlinelibrary.wiley.com/doi/10.1155/2022/7675925 https://doi.org/10.1155/2022/7675925
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score	13.211869

Towards automated multiclass severity prediction approach for COVID-19 infections based on combinations of clinical data

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