Study of impact of COVID-19 on different age groups using machine learning classifiers. Issue 2 (17th February 2021)

• Record Type: Journal Article
• Authors: Patil, Harshal; Sharma, Shilpa; Raja, Linesh
• Is Part Of: Journal of interdisciplinary mathematics. Volume 24:Issue 2(2021)
• Page Start: 479
• Page End: 487
• Publication Date: 2021-02-17
• Subjects: 62J05; COVID-19 -- Machine learning -- KNN -- XGBoost -- Random forest -- Decision tree; Mathematics -- Periodicals; Mathematics; Periodicals; 510.5
• DOI: 10.1080/09720502.2021.1896585
• Languages: English
• ISSNs: 0972-0502

Abstract

Abstract: The global disaster outbreak, known as COVID-19-Severe Acute Respiratory Syndrome (Coronavirus), has posed a global threat to living society. The whole world is making incredible efforts in terms of infrastructural, finance, sources of data, protective equipment, life-risk and several other resources to combat the propagation of this deadly disease. Artificial intelligence researchers are focusing their knowledge on deriving a mathematical model to evaluate this pandemic situation using nationally/globally shared data. In order to contribute to the well-being of a living community, this article proposes to use machine learning algorithms on the available pandemic dataset to recognize different age groups which are most likely to be impacted by coronavirus disease. Diverse predictive models are constructed by considering a variety of machine learning methods and their efficiency is measured and computed. The predictive model based on Random Forest Regressor and Random Forest Classifier performed better as compared to predictive models based on Naïve Bayes Classifier, SVM, Multi-linear Regression, Logistic Regression and XGBoost Classifier, Decision Tree Classifier and KNN.