Prediction of Eligibility for Covid-19 Vaccine Using SMLT Technique

Prajwal Bisht; Vinayak Bora; S. Poornima; M. Pushpalatha

doi:10.4028/p-6a0n6u

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HomeAdvances in Science and TechnologyAdvances in Science and Technology Vol. 124Prediction of Eligibility for Covid-19 Vaccine...

Prediction of Eligibility for Covid-19 Vaccine Using SMLT Technique

Abstract:

The worldwide society was devastated by the 2019 coronavirus illness (COVID19) epidemic in Wuhan, China, which overloaded advanced medical systems around the world. The World Health Organization (WHO) is constantly monitoring and responding to the pandemic. The current rapid and exponential development in patient numbers necessitates the use of AI technology to forecast possible outcomes of infected individuals in order to provide suitable therapy. The goal is to find the machine learning-based solution that best fits the Covid19 vaccination predictions with the highest accuracy. Variable identification, univariate analysis, bivariate and multivariate analysis, missing value handling and data validation analysis, data cleaning / preparation, and data validation analysis are all accomplished using supervised machine learning technology (SMLT). Various types of data, such as visualisation, are gathered. For the entire given dataset. Proposal of a machine learning-based method for accurately predicting the suitability of Covid19 vaccine prediction.

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Periodical:

Advances in Science and Technology (Volume 124)

Pages:

462-468

DOI:

https://doi.org/10.4028/p-6a0n6u

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Online since:

February 2023

Authors:

Prajwal Bisht*, Vinayak Bora, S. Poornima, M. Pushpalatha

Keywords:

Blood Pressure, Covid-19, Data Exploration, Decision Tree, Eligibility, F1-Metric, Logistic Regression, Machine Learning, Model, Prediction, Random Forest, Sugar, Support Vector Machine, Vaccine

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