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

• 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
• Institution: Universiti Teknikal Malaysia Melaka

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.