COVID-19 infection prediction using physical signs

Zirui Wen; Junjie Zhang; Yuhao Zhang

doi:10.1117/12.2640976

13 October 2022 COVID-19 infection prediction using physical signs

Zirui Wen, Junjie Zhang, Yuhao Zhang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872C (2022) https://doi.org/10.1117/12.2640976
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Recently, COVID-19 has become a world-wide pandemic with high infection and death rate. Researches mainly focus on how to predict the trend or control the infection. In this paper, we aim to predict COVID-19 pandemic using physical signs. We apply three methods, including decision tree, random forest and supported vector machine. We claim that all the methods achieves a satisfying performance, with the top accuracy is 99.8%. Our method provides a significant contribution in prediction COVID-19 infection, which will apply in real-world application with a real-time inference time.

Citation Download Citation

Zirui Wen, Junjie Zhang, and Yuhao Zhang "COVID-19 infection prediction using physical signs", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872C (13 October 2022); https://doi.org/10.1117/12.2640976

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