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ISSN 2063-5346
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PROVIDING AN EFFICIENT COVID-19 PREDICTION MODEL TO IMPROVE CLASSIFICATION USING DEEP LEARNING AND MACHINE LEARNING CLASSIFIER

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Chander Deep Singh1*, Dr. Dinesh Kumar Garg2 , Vishali Sharma
» doi: 10.48047/ecb/2023.12.si5a.0341

Abstract

An outbreak of unidentified infections known as COVID-19 primarily affected the circulatory tract. The illness spreads throughout the entire world and eventually gets worse significantly declining population. The well developed & perfected Covid-19 prediction method is still debatable. In this article, we suggested using affordable X-ray scans to diagnose COVID-19 patients. The majority of healthcare facilities have access to Xray imaging contrasted to other imaging techniques. To use machine learning and deep learning models CNN with the goal of analyzing its regular exponential behaviour as well as making predictions about the COVID2019's potential reach across countries by using real-time data. On the premise of accuracy, class specifications TP rate, FP rate, precision, recall, as well as F-measure, the suggested CNN is validated to other existing methods Naive Bayes, Support Vector Machine, Random Forest, as well as decision tree. The suggested method's accuracy is 99.07 percent.

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