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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Evaluation of using Parametric and Non-parametric Machine Learning Algorithms for Covid-19 Forecasting

Author 1: Ghada E. Atteia
Author 2: Hanan A. Mengash
Author 3: Nagwan Abdel Samee

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121071

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 10, 2021.

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Abstract: Machine learning prediction algorithms are considered powerful tools that could provide accurate insights about the spread and mortality of the novel Covid-19 disease. In this paper, a comparative study is introduced to evaluate the use of several parametric and non-parametric machine learning methods to model the total number of Covid-19 cases (TC) and total deaths (TD). A number of input features from the available Covid-19 time sequence are investigated to select the most significant model predictors. The impact of using the number of PCR tests as a model predictor is uniquely investigated in this study. The parametric regression including the Linear, Log, Polynomial, Generative Additive Regression, and Spline Regression and the non-parametric K-Nearest Neighborhood (KNN), Support Vector machine (SVM) and the Decision Tree (DT) have been utilized for building the models. The findings show that, for the used dataset, the linear regression is more accurate than the non-parametric models in predicting TC & TD. It is also found that including the total number of tests in the mortality model significantly increases its prediction accuracy.

Keywords: Covid-19; parametric regression; non-parametric regression; linear regression; log regression; polynomial regression; generative additive regression; spline regression; k-nearest neighborhood; KNN; support vector machine; SVM; decision trees; DT

Ghada E. Atteia, Hanan A. Mengash and Nagwan Abdel Samee, “Evaluation of using Parametric and Non-parametric Machine Learning Algorithms for Covid-19 Forecasting” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121071

@article{Atteia2021,
title = {Evaluation of using Parametric and Non-parametric Machine Learning Algorithms for Covid-19 Forecasting},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121071},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121071},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Ghada E. Atteia and Hanan A. Mengash and Nagwan Abdel Samee}
}


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