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 (Hindawi Limited, 2022)
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.