Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

Ahmed M. Dinar, Enas A. Raheem, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Marwan Ghazi Oleiwie, Fawzi Hasan Zayr, Omar Al-Boridi, Mohammed Nasser Al-Mhiqani, Mohammed Nasser Al-Andoli

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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 SpO2 (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-19 patients were recruited from the Azizia 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 of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths.

Original languageEnglish
Article number7675925
Number of pages8
JournalMobile Information Systems
Early online date8 Jul 2022
Publication statusPublished - 1 Sep 2022
Externally publishedYes

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