Distinction of COVID-19 and Analysis on Symptoms and Hospitalization Time

Xiran Gao; Guang Ni; Jingyuan Zhang; Xiaoning Zhao

doi:10.2991/aebmr.k.220307.076

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Distinction of COVID-19 and Analysis on Symptoms and Hospitalization Time

Authors

Xiran Gao¹^{, †}, Guang Ni²^{, †}, Jingyuan Zhang³^{, *}^{, †}, Xiaoning Zhao⁴^{, †}

¹Maple Leaf International high school, Zhenjiang, Jiangsu Province, 212002, China

²Business administration college, University of Pittsburgh, Pittsburgh, Pennsylvania, 15260, in USA

³Mathematical science college, Jiangsu University, Zhenjiang, Jiangsu Province, 212013, China

⁴St.John college, Durham University, Durham, DH1 3RJ, UK

^†

These authors contributed equally.

^*Corresponding author email E-mail: 3190113023@stmail.ujs.edu.cn

Corresponding Author

Jingyuan Zhang

Available Online 26 March 2022.

DOI: 10.2991/aebmr.k.220307.076 How to use a DOI?
Keywords: COVID-19; Distinction; Analysis; Machine learning
Abstract: This paper aims to use the machine learning model to distinguish more precisely whether the patients get COVID-19 or not and analyze symptoms and hospitalization time of the patients. We use CNN to test the hypothesis: we can find from their X-rays that whether the patients get COVID-19. The result showed a 95 percent accuracy indicates that it can be found who are infected with COVID-19 from the model easily. It suggests that X-ray is an important and accurate indicator to find COVID-19 since respectively, X-rays results from patients with COVID-19 and normal people differ significantly. In addition, after the analysis of symptoms and time of staying in hospital, we found that patients were not likely to had no symptoms or experiencing and who had one of the symptoms accounted for the largest group of patients. The symptoms or experiencing they behaved had exact combinations rather than randomly combined, like someone may have fever, tiredness and dry-cough at the same time but he cannot have fever, dry-cough and difficulty-in-breathing simultaneously. What is more, the result also showed that the elder the patients, the longer they stayed in hospitals. The CNN model used in this study has higher accuracy. In addition, the result can help the hospitals effectively avoid the over concentration of medical resources and allocate them reasonably.
Open Access: This is an open access article under the CC BY-NC license.

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Volume Title: Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
Series: Advances in Economics, Business and Management Research
Publication Date: 26 March 2022
ISBN: 978-94-6239-554-1
ISSN: 2352-5428
DOI: 10.2991/aebmr.k.220307.076 How to use a DOI?
Open Access: This is an open access article under the CC BY-NC license.

Cite this article

ris enw bib

TY  - CONF
AU  - Xiran Gao
AU  - Guang Ni
AU  - Jingyuan Zhang
AU  - Xiaoning Zhao
PY  - 2022
DA  - 2022/03/26
TI  - Distinction of COVID-19 and Analysis on Symptoms and Hospitalization Time
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 471
EP  - 480
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.076
DO  - 10.2991/aebmr.k.220307.076
ID  - Gao2022
ER  -

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