Hybrid Optimization Enabled Neural Machine Learning Model for Covid-19 Prediction Across India

Abstract

The detailed investigation of Covid19 prediction and analysis using machine learning algorithms is proposed in this paper. A recently identified coronavirus is the cause of the infectious condition known as coronavirus disease, or COVID-19. In December 2019, the newly detected coronavirus that causes this disease was initially identified. Over 7 million individuals contracted the sickness, and 0.40 million people died from it within that same year. In India, the first coronavirus case was reported on January 30, 2020, and between February 15, 2020, and July 19, 2021, 31,219,374 cases were impacted by COVID-19. Simply contacting an infected person, an item or thing contaminated with the coronavirus, or an infected person themselves can spread this disease. Simply contacting an infected person, an object or thing contaminated with the coronavirus, or an infected person’s mouth, eyes, etc. can spread this disease. According to pathologists’ investigations, the COVID-19 virus has striking similarities to the coronaviruses that cause Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Algorithms for machine learning (ML) have demonstrated their value in predicting perioperative outcomes to enhance decision-making on future actions. Many application domains that need the identification and prioritisation of negative aspects for a threat have long employed machine learning algorithms. recovered, and COVID-19-related fatalities in India. Numerous prediction techniques are widely employed to address forecasting issues. The number of confirmed, recovered, and fatal COVID-19 cases in India is predicted using a machine learning algorithm.