In Frontiers in cell and developmental biology
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.
Yao Haochen, Zhang Nan, Zhang Ruochi, Duan Meiyu, Xie Tianqi, Pan Jiahui, Peng Ejun, Huang Juanjuan, Zhang Yingli, Xu Xiaoming, Xu Hong, Zhou Fengfeng, Wang Guoqing
COVID-19, biomarkers, blood and urine tests, model, severity detection