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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1002/spe.2969
|2 doi
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|a pubmed24n1517.xml
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|a (DE-627)NLM327767979
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Mohan, Senthilkumar
|e verfasserin
|4 aut
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| 245 |
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|a An approach to forecast impact of Covid-19 using supervised machine learning model
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 30.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 John Wiley & Sons, Ltd.
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|a The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix
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|a Journal Article
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4 |
|a Covid‐19
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| 650 |
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4 |
|a ensemble learning
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| 650 |
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4 |
|a healthcare
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a prediction
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| 700 |
1 |
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|a A, John
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Abugabah, Ahed
|e verfasserin
|4 aut
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| 700 |
1 |
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|a M, Adimoolam
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kumar Singh, Shubham
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kashif Bashir, Ali
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sanzogni, Louis
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Software: practice & experience
|d 1998
|g 52(2022), 4 vom: 22. Apr., Seite 824-840
|w (DE-627)NLM098130218
|x 0038-0644
|7 nnns
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| 773 |
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|g volume:52
|g year:2022
|g number:4
|g day:22
|g month:04
|g pages:824-840
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|u http://dx.doi.org/10.1002/spe.2969
|3 Volltext
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