Evaluation of using Parametric and Non-parametric Machine Learning Algorithms for Covid-19 Forecasting
| Autor: | Ghada E. Atteia, Nagwan M. Abdel Samee, Hanan A. Mengash |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Polynomial regression
General Computer Science business.industry Computer science Nonparametric statistics Decision tree Machine learning computer.software_genre Logistic regression Nonparametric regression Support vector machine Linear regression Artificial intelligence business computer Parametric statistics |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 12 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2021.0121071 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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