Implementation of Machine Learning Model On SARS-Cov-2 Clinical Evidence

Venu Paritala¹ , S Ranjeeth² , Harsha Thummala³

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.3 , pp.31-35, Jun-2022

Online published on Jun 30, 2022

Copyright © Venu Paritala, S Ranjeeth, Harsha Thummala . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Computational strategies for machine learning (ML) have appeared their meaning for the projection of potential comes about for educated decisions. Machine learning algorithms have been connected for a long time in numerous applications requiring the discovery of antagonistic hazard variables. This ponder appears the capacity to anticipate the number of people who are influenced by the SARS-CoV-2 as a potential danger to human creatures by ML demonstrating. As an alternative to optimization, statistical, and neural network models, this research offers a relative analysis of machine learning and delicate computing models to anticipate the SARS-CoV-2 outbreak.Among a wide extend of machine learning models explored, three models appeared promising comes about .In this Module used parameters of entities cumulative total of cases and cumulative total of deaths reported globally in this module prediction. We are predicting the newly reported cases in past 24hrs, newly reported cases in past 7days and newly reported deaths in last 24hrs, newly reported deaths in past 7days, etc. In Machine learning it`s play`s a significance role in the prediction of covid 19 cases. Using these techniques easily identified SARS-COV-2 patient growth rate, death rate, Recovery rate, etc., in the Last 24 hours, 7 days, and also a mode of Transmission at countrywide. The models outcomes 93.6 accuracy. (Its show`s high amount of accuracy in testing .optimization module is useful to prediction of cases which are going happen in future).

Key-Words / Index Term :
SARS-COV-2; Statistical; Neural Network; Optimization; Machine Learning

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