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: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022
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| 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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| Summary: | 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. |
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