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The Prediction Model for Classification of COVID-19 Infected Patients Using Vital Sign | IEEE Conference Publication | IEEE Xplore

The Prediction Model for Classification of COVID-19 Infected Patients Using Vital Sign


Abstract:

Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 and is now spreading worldwide. World Health Organization (WHO) declared a pandemic on March 11, ...Show More

Abstract:

Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 and is now spreading worldwide. World Health Organization (WHO) declared a pandemic on March 11, 2020. Worldwide, the number of infections and deaths from COVID-19 is increasing. As of August 1, 2021, there were 196,553,009 confirmed cases of COVID-19 worldwide and 4,200,412. This paper proposes a classification model that uses a machine learning model based on vital signs to predict patients with COVID-19 infection. We propose the use of Decision Tree, XGBoost, and Support Vector Machine (SVM) with ensemble methods that combine several good learning models to improve predictivity. And based on infectious symptoms such as fever, shortness of breath, etc. announced by WHO, we also propose a model that uses body temperature, heart rate, and SPo2 data to predict and determine whether a person is infected or not. These studies are expected to help reduce the spread of COVID-19 by initially classifying suspected infected patients and reduce mortality through treatment in early detection of infected patients.
Date of Conference: 20-22 October 2021
Date Added to IEEE Xplore: 07 December 2021
ISBN Information:
Print on Demand(PoD) ISSN: 2162-1233
Conference Location: Jeju Island, Korea, Republic of

Funding Agency:


References

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