The Number of Confirmed Cases of Covid-19 by using Machine Learning : Methods and Challenges
© CIMNE, Barcelona, Spain 2020..
Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Archives of computational methods in engineering : state of the art reviews - 28(2021), 4 vom: 23., Seite 2645-2653 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ahmad, Amir [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Revised 18.02.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s11831-020-09472-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM314106243 |
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| 520 | |a Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19 | ||
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| 700 | 1 | |a Barukab, Omar Mohammed |e verfasserin |4 aut | |
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