| Autor: |
Josephat Kalezhi, Mathews Chibuluma, Christopher Chembe, Victoria Chama, Francis Lungo, Douglas Kunda |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Results in Engineering, Vol 13, Iss , Pp 100363- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1230 |
| DOI: |
10.1016/j.rineng.2022.100363 |
| Popis: |
The outbreak of Covid-19 pandemic has been declared a global health crisis by the World Health Organization since its emergence. Several researchers have proposed a number of techniques to understand how the pandemic affects the populations. Reported among these techniques are data mining models which have been successfully applied in a wide range of situations before the advent of Covid-19 pandemic. In this work, the researchers have applied a number of existing data mining methods (classifiers) available in the Waikato Environment for Knowledge Analysis (WEKA) machine learning library. WEKA was used to gain a better understanding on how the epidemic spread within Zambia. The classifiers used are J48 decision tree, Multilayer Perceptron and Naïve Bayes among others. The predictions of these techniques are compared against simpler classifiers and those reported in related works. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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