Frontiers in Cell and Developmental Biology (Jul 2020)

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

  • 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

DOI
https://doi.org/10.3389/fcell.2020.00683
Journal volume & issue
Vol. 8

Abstract

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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.

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