Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study

Xianlong Zhou,^1,* Zhichao Wang,^2,* Shaoping Li,¹ Tanghai Liu,³ Xiaolin Wang,⁴ Jian Xia,¹ Yan Zhao<sup>1

1</sup>Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, People’s Republic of China; ²Emergency Department, Wuhan No. 1 Hospital, Wuhan, Hubei, 430022, People’s Republic of China; ³Information Center, Wuhan No. 1 Hospital, Wuhan, Hubei, 430022, People’s Republic of China; ⁴Gennlife (Beijing) Biotechnology Co. Ltd, Haidian, Beijing, 100080, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yan Zhao
Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, People’s Republic of China
Email doctoryanzhao@whu.edu.cn
Jian Xia
Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, People’s Republic of China
Email jianjian_1998@sina.com

Background: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens.
Methods: Laboratory-confirmed COVID-19 and influenza patients between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms.
Results: Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 7:3, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI: 0.81, 0.87), respectively.
Conclusion: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens.

Keywords: COVID-19, influenza, classification, machine learning, diagnostic accuracy