Hybrid Machine-Learning Models for Predicting COVID-19

Authors

  • Dr. Harsh Mathur  Associate Professor (CSE Department), Rabindra Nath Tagore University, Bhopal, Madhya Pradesh, India
  • Vikas Kumar   Research Scholar(CSE Department), Rabindra Nath Tagore University, Bhopal, Madhya Pradesh, India

Keywords:

Naive Bayes, Voting Classifier, Logistic Regression, Precision , Recall

Abstract

COVID-19 dataset comprises time, nation, established cases, no. of recovered people, overall mortality rate. The data is integrated with climate data consisting of dampness, dew, ozone, awareness, highest and lowest temperature etc. Several online websites are present to collect the data. Some of these websites include “World meters”, “Our World in Data”, “World Bank Open Data”, and the official website of the World Health Organization (WHO). Moreover, researchers focus on human development reports for collecting other kind of data. The artificial intelligence based COVID-19 diagnosis strategies can generate more accurate results, save radiologist time, and make the diagnosis process cheaper and faster than the usual laboratory techniques. The COVID-19 detection has many stages like pre-processing, feature extraction, classification and performance analysis. In this work, a voting classification method is designed for the covid-19 prediction. It is analysed that proposed model increase accuracy, precision and recall for the covid-19 prediction.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Harsh Mathur, Vikas Kumar , " Hybrid Machine-Learning Models for Predicting COVID-19, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.131-144, March-April-2023.