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SARS-CoV-2 Future Forecasting Using Multi-Linear Regression Model
International Journal of Intelligent Systems and Applications in Engineering ; 10(2):159-165, 2022.
Article in English | Scopus | ID: covidwho-1898095
ABSTRACT
The 2019 pandemic in Wuhan, China caused a devastating global outbreak of the Coronavirus Disease (SARSCoV-2). Machine learning offers a number of prediction models for future events that are based on training and testing, including conventional machine learning and Deep Learning. This study shows that machine-learning models can anticipate the number of future SARS-CoV-2 patients that are currently seen as a possible risk to the human race. Supervised machine learning models like linear regression, vector support and regression tree are used for prediction. Data on the total cases and recovery cases are based on two types of predictions new infections and recovery situations. The machine-learning regression model is used to generate the outcome. In this paper, we present prediction of future forecasting of Covid cases based on current situation by applying dataset of before and after pre-trial vaccine. © 2022, Ismail Saritas. All rights reserved.
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Collection: Databases of international organizations Database: Scopus Language: English Journal: International Journal of Intelligent Systems and Applications in Engineering Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Language: English Journal: International Journal of Intelligent Systems and Applications in Engineering Year: 2022 Document Type: Article