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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), Frontiers in Cell and Developmental Biology
- Publication Year :
- 2020
- Publisher :
- Frontiers Media SA, 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.
- Subjects :
- 0301 basic medicine
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
medicine.disease_cause
Machine learning
computer.software_genre
Cell and Developmental Biology
03 medical and health sciences
0302 clinical medicine
medicine
blood and urine tests
lcsh:QH301-705.5
Human society
Original Research
Coronavirus
model
business.industry
severity detection
COVID-19
biomarkers
Cell Biology
medicine.disease
Support vector machine
Pneumonia
030104 developmental biology
lcsh:Biology (General)
030220 oncology & carcinogenesis
Artificial intelligence
business
computer
Developmental Biology
Subjects
Details
- Language :
- English
- ISSN :
- 2296634X
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Frontiers in Cell and Developmental Biology
- Accession number :
- edsair.doi.dedup.....d39ded40abed29ca3d6e8acc28bccf85
- Full Text :
- https://doi.org/10.3389/fcell.2020.00683