Prevalence Of Logistic Regression Analysis In Covid-19
Abstract
In December 2019, an unconventional corona virus presently known as COVID-19 was found to be emanating from Wuhan, China. This unusual virus also known as SARS-CoV-2 has proliferated worldwide ushering into a Global Pandemic. As of 21st May 2020, WHO tracked a record of 4,893,186 as confirmed cases with 323,256 deaths for 216 countries worldwide, thus this obligates an opportune and a prompt model for forecasting ahead.
Some prominent technologies like Artificial Intelligence and Machine Learning are putting their best way forward in prediction analysis and in medicine and healthcare. Logistic Regression, an inventive machine learning algorithm can engender accurate probable occurrences for the risk of COVID-19 specifically. This paper aims to analyse and showcase the prevalence of Logistic Regression in the forecasting of COVID-19 with the intention to induce discrete and accurate results
