| Autor: |
Haochen Yao, Nan Zhang, Ruochi Zhang, Meiyu Duan, Tianqi Xie, Jiahui Pan, Ejun Peng, Juanjuan Huang, Yingli Zhang, Xiaoming Xu, Hong Xu, Fengfeng Zhou, Guoqing Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Frontiers in Cell and Developmental Biology, Vol 8 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2296-634X |
| DOI: |
10.3389/fcell.2020.00683 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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