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

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

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: COVID-19, Multiclass prediction, Prediction systems
Divisions: Faculty of Information and Communication Technology
Depositing User: Norfaradilla Idayu Ab. Ghafar
Date Deposited: 09 Oct 2024 09:56
Last Modified: 09 Oct 2024 09:56
URI: http://eprints.utem.edu.my/id/eprint/27779
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