Prediction of COVID-19 based on machine learning using cartographic variables

Kavitha Soppari *, K. Chinmayi, G. Srikanth, K. Bhavani Reddy and CH Ramanavasu

Department of Computer Science and Engineering, ACE Engineering College Hyderabad Telangana, India.
 
Review
International Journal of Science and Research Archive, 2023, 09(02), 163–170.
Article DOI: 10.30574/ijsra.2023.9.2.0511
Publication history: 
Received on 19 May 2023; revised on 07 July 2023; accepted on 10 July 2023
 
Abstract: 
The Coronavirus has rapidly spread to all parts of the world. Research is continuing to find a cure for this disease while there is no exact reason for this outbreak. As the number of cases to test for Coronavirus is increasing rapidly day by day, it is impossible to test due to the time and cost factors. Over recent years, machine learning has turned very reliable in the medical field. Using machine learning to predict COVID-19 in patients will reduce the time delay for the results of the medical tests and modulate health workers to give proper medical treatment to them.
A Systematic Literature Review is performed to identify the most suitable algorithms for the prediction model. Then through the findings of the literature study, an experimental model is developed for prediction of COVID-19 and to identify the features that impact the model.
Based on cartographic variables, it is possible to make predictions about the spread and impact of COVID-19 in different regions. These variables can include population density, age distribution, healthcare infrastructure, and mobility patterns.
Overall, a combination of cartographic variables can be used to develop predictive models that can help public health officials and policymakers better understand the trajectory of the pandemic and make informed decisions about resource allocation and mitigation strategies.
 
Keywords: 
COVID-19; Machine Learning; Prediction; Classification Algorithims Techniques; Naive bayes classifier algorithm
 
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0