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Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests
- Source :
- Frontiers in Cell and Developmental Biology, Vol 8 (2020)
- Publication Year :
- 2020
- Publisher :
- Frontiers Media S.A., 2020.
-
Abstract
- The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.
Details
- Language :
- English
- ISSN :
- 2296634X
- Volume :
- 8
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Cell and Developmental Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2c0dc0efa2b84d3fa2fe925a62a646ac
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fcell.2020.00683