Deep Analysis of COVID-19 Pandemic using Machine Learning Techniques

Abstract

There was a first covid-19 case reported on 30 January 2020. And after March 2020, the number of cases kept increasing. (SARS-CoV-2) Severe Acute Respiratory Syndrome Catastrophic outbreak of Coronavirus is also known as COVID-2019. It was a threat to a worldwide living society. This pandemic worldwide is measured as COVID-2019 and using different examinations and various numerical models to estimate the pandemic. These numerical models are dependent on various factors, potential orientation, and studies. Recently, a model was presented that can be valuable for the spread of the predicting COVID-2019, in which machine learning models time series forecasting techniques such as Support Vector Regression, linear regression, and DST learning forecasting models such as (LSTM). This study defines how deep learning COVID-19 provides useful and precise solutions instead of the features traditionally computer-based technique. Moreover, in this technique, the risk prediction for healthcare is beneficial during this COVID-19 crisis. It also evaluates the machine learning risk factors allowing for age, location, social customs, and temperature. We initiate through estimating the current state of in-depth learning and end through the limitations of COVID-19 applications for intensive learning.