AUTHOR=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 TITLE=Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests JOURNAL=Frontiers in Cell and Developmental Biology VOLUME=8 YEAR=2020 URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2020.00683 DOI=10.3389/fcell.2020.00683 ISSN=2296-634X 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.