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.
【저자키워드】 COVID-19, Biomarkers, Model, severity detection, blood and urine tests, 【초록키워드】 coronavirus, Biomarker, Pneumonia, risk, China, Accuracy, outbreak, SVM, mechanism, machine learning algorithm, COVID-19 patient, COVID-19 infections, severe symptoms, Final, human host, contagiousness, organ failures, feature, develop, caused, carried, significantly, involved, investigated, screened, facilitate, demonstrated, build, 【제목키워드】 machine learning, Test, detection,