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Non-NAT Definite Diagnosis Models of COVID-19 Based on Hematological Features

15 Pages Posted: 14 Dec 2020

See all articles by Miao Li

Miao Li

Huazhong University of Science and Technology - School of Computer Science and Technology

Shijun Lei

Huazhong University of Science and Technology - Department of Clinical Laboratory

Long Hu

Huazhong University of Science and Technology - School of Computer Science and Technology

Yixue Hao

Huazhong University of Science and Technology - School of Computer Science and Technology

Jun Yang

Wuhan University of Technology - School of Information Engineering

Jian Wang

Huazhong University of Science and Technology - Department of Clinical Laboratory

Qianwen Zhang

Huazhong University of Science and Technology - Department of Clinical Laboratory

Rui Wang

Huazhong University of Science and Technology - School of Computer Science and Technology

Wenjing Xiao

Huazhong University of Science and Technology - NHC Key Laboratory of Pulmonary Diseases

Yingying Jiang

Huazhong University of Science and Technology - School of Computer Science and Technology

Peilin Wang

Wuhan University - Department of Psychiatry

Kai Huang

The Chinese University of Hong Kong (CUHK) - Shenzhen Institute of Artificial Intelligence and Robotics for Society

Zhongchun Liu

Wuhan University - Department of Psychiatry

Lin Wang

Huazhong University of Science and Technology - Research Center for Tissue Engineering and Regenerative Medicine

Zheng Wang

Huazhong University of Science and Technology - Research Center for Tissue Engineering and Regenerative Medicine

Min Chen

Huazhong University of Science and Technology - School of Computer Science and Technology

More...

Abstract

Background: Given that 2019 novel coronavirus (COVID-19) spreads rapidly, it is critical to make rapid and accurate detection of COVID-19 patients towards containment of SARS-CoV-2 virus. At present, COVID-19 patients are mainly identified through viral nuclear acid testing (NAT). However, factors such as time for patients being tested, experience of test operators, and specimen’s preparation, might affect the accuracy of testing results. The purpose of this study was to use different classification and feature selection methods to improve the diagnostic accuracy of COVID-19 patients. 

Methods: We utilized seven machine learning algorithms for assisting diagnosis of COVID-19 by developing a non-NAT algorithm. In order to reduce the number of input features while maintaining the models’ performance so as to decrease the cost and time consumption, we adopted three algorithms, such as Chi-square test, variance analysis, and feature importance tests to identify the optimal feature sets. 

Findings: The XGBoost and RF models displayed the best performance for COVID-19 detection, with the highest accuracy rate more than 0·96. The accuracy of RF model was 0·968 when using only ten hematological features and body temperature. 

Interpretation: Ten blood features and body temperature can fairly accurately determine whether a suspected patient is infected with COVID-19. Our model can improve the diagnostic accuracy of COVID-19 and reduce the spread. 

Funding: This work is supported by grants from the National Key Research and Development Program of China under Grant 2017YFE0123600, the Natural Science Foundation of China (81873931, 81974382 and 81773104), the Frontier Exploration Program of Huazhong University of Science and Technology (2015TS153), and the Major Scientific and Technological Innovation Projects in Hubei Province (2018ACA136).

Declaration of Interests: All the authors stated that the paper had never been published elsewhere, and that there were no competing economic interests.

Ethics Approval Statement: The collection, use, and retrospective analysis of chest CT images, CFs and SARS-CoV-2 nucleic acid PCR results of patients were approved by the institutional ethical committees of HUST-UH (IRB ID: [2020] IEC(A001)).

Keywords: COVID-19; non-NAT; machine learning; hematological features; optimum feature set

Suggested Citation

Li, Miao and Lei, Shijun and Hu, Long and Hao, Yixue and Yang, Jun and Wang, Jian and Zhang, Qianwen and Wang, Rui and Xiao, Wenjing and Jiang, Yingying and Wang, Peilin and Huang, Kai and Liu, Zhongchun and Wang, Lin and Wang, Zheng and Chen, Min, Non-NAT Definite Diagnosis Models of COVID-19 Based on Hematological Features. Available at SSRN: https://ssrn.com/abstract=3748332 or http://dx.doi.org/10.2139/ssrn.3748332

Miao Li

Huazhong University of Science and Technology - School of Computer Science and Technology

1037 Luoyu Road
Wuhan, Hubei 430074
China

Shijun Lei

Huazhong University of Science and Technology - Department of Clinical Laboratory ( email )

China

Long Hu

Huazhong University of Science and Technology - School of Computer Science and Technology ( email )

1037 Luoyu Road
Wuhan, Hubei 430074
China

Yixue Hao

Huazhong University of Science and Technology - School of Computer Science and Technology ( email )

1037 Luoyu Road
Wuhan, Hubei 430074
China

Jun Yang

Wuhan University of Technology - School of Information Engineering

Wuhan
China

Jian Wang

Huazhong University of Science and Technology - Department of Clinical Laboratory

China

Qianwen Zhang

Huazhong University of Science and Technology - Department of Clinical Laboratory ( email )

China

Rui Wang

Huazhong University of Science and Technology - School of Computer Science and Technology

1037 Luoyu Road
Wuhan, Hubei 430074
China

Wenjing Xiao

Huazhong University of Science and Technology - NHC Key Laboratory of Pulmonary Diseases

Wuhan, 430022
China

Yingying Jiang

Huazhong University of Science and Technology - School of Computer Science and Technology

1037 Luoyu Road
Wuhan, Hubei 430074
China

Peilin Wang

Wuhan University - Department of Psychiatry

Wuhan
China

Kai Huang

The Chinese University of Hong Kong (CUHK) - Shenzhen Institute of Artificial Intelligence and Robotics for Society

Shatin, N.T.
Hong Kong
Hong Kong

Zhongchun Liu

Wuhan University - Department of Psychiatry ( email )

Wuhan
China

Lin Wang

Huazhong University of Science and Technology - Research Center for Tissue Engineering and Regenerative Medicine ( email )

China

Zheng Wang

Huazhong University of Science and Technology - Research Center for Tissue Engineering and Regenerative Medicine ( email )

China

Min Chen (Contact Author)

Huazhong University of Science and Technology - School of Computer Science and Technology ( email )

1037 Luoyu Road
Wuhan, Hubei 430074
China

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