Predicting Novel Coronavirus Trends Using Machine Learning

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Author(s): Anamika Ahirwar (Compucom Institute of Technology and Management, Jaipur, India)and Mahendra Singh Panwar (Compucom Institute of Technology and Management, Jaipur, India)
Copyright: 2025
Pages: 28
Source title: Driving Global Health and Sustainable Development Goals With Smart Technology
Source Author(s)/Editor(s): Mohit Kukreti (University of Technology and Applied Sceinces, Oman), Sabina Sehajpal (Chandigarh University, India), Rajesh Tiwari (Graphic Era University, India)and Kiran Sood (Chitkara University, India)
DOI: 10.4018/979-8-3373-0240-9.ch017

Keywords: Environment & Agriculture / Information Science Reference / Public Health and Welfare / Sustainable Development

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Abstract

2020 began with the advent of disruption brought on by a new virus called SARS-CoV-2. The coronavirus pandemic i.e. COVID-19, according to the World Health Organization (WHO), is putting a lot of strain on even the strongest healthcare systems in the entire world. Our current study explores supervised learning methods in machine learning, such as SVMachine, K-nearest neighbor, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and a newly developed algorithm called XGB classifier. Specifically, the prediction of COVID-19-related deaths and recoveries is the focus of our proposed approach. A GitHub repository served as the source of the dataset used in this investigation. In this paper our aim is to enhance our comprehension of the pandemic's consequences through the application of machine learning techniques.

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Predicting Novel Coronavirus Trends Using Machine Learning

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