Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading

Nastiti, FE; Musa, S; Yafi, E; Chauhan, R

Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading

Nastiti, FE Musa, S Yafi, E

Chauhan, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), 2023, 00, pp. 1-5
Issue Date:: 2023-02-06

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Field	Value	Language
dc.contributor.author	Nastiti, FE
dc.contributor.author	Musa, S
dc.contributor.author	Yafi, E https://orcid.org/0000-0002-3144-0784
dc.contributor.author	Chauhan, R
dc.date	2022-10-08
dc.date.accessioned	2024-03-08T04:14:25Z
dc.date.available	2024-03-08T04:14:25Z
dc.date.issued	2023-02-06
dc.identifier.citation	2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), 2023, 00, pp. 1-5
dc.identifier.isbn	9781665453950
dc.identifier.uri	http://hdl.handle.net/10453/176355
dc.description.abstract	The dataset methods and machine learning prediction framework on the Covid 19 theme have been published widely and complex Special publications on the spread of virus infection 19 in the form of a time series need to be mapped more comprehensively This literature review aims to identify and analyze research trends datasets and methods used in predicting Covid 19 with Machine Learning Engineering research between 2019 and 2021 Identifying the need specifying the research question evaluating review protocol searching for papers scanning papers and reporting results are the eight major steps of this systematic literature review The most critical aspect of systematic analysis is defining the research questions The PICOC techniques are used to identify research questions Journal candidates were filtered out using inclusion and exclusion criteria techniques to shrink the SLR scope area based on a literature study it was found that research in 2019 2021 on the Covid 19 distribution prediction system used variables susceptibility infection mortality geography weather and patient clinical data to be processed into ANFIS machine learning prediction models and neural networks are several models A classification model that is widely used for hybrid processing in calculating covid 19 infection prediction The datasets that are often used do not fully meet the epidemiological aspects that trigger the spread of COVID 19 infections ANFIS and NN are several classification methods that are widely used for hybrid processing in calculating predictions of the spread of COVID 19 infection
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2022 4th International Conference on Cybernetics and Intelligent System (ICORIS)
dc.relation.ispartof	2022 4th International Conference on Cybernetics and Intelligent System
dc.relation.isbasedon	10.1109/icoris56080.2022.10031414
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Prapat, Indonesia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-08T04:14:24Z
pubs.finish-date	2022-10-09
pubs.publication-status	Published
pubs.start-date	2022-10-08
pubs.volume	00

Abstract:

The dataset methods and machine learning prediction framework on the Covid 19 theme have been published widely and complex Special publications on the spread of virus infection 19 in the form of a time series need to be mapped more comprehensively This literature review aims to identify and analyze research trends datasets and methods used in predicting Covid 19 with Machine Learning Engineering research between 2019 and 2021 Identifying the need specifying the research question evaluating review protocol searching for papers scanning papers and reporting results are the eight major steps of this systematic literature review The most critical aspect of systematic analysis is defining the research questions The PICOC techniques are used to identify research questions Journal candidates were filtered out using inclusion and exclusion criteria techniques to shrink the SLR scope area based on a literature study it was found that research in 2019 2021 on the Covid 19 distribution prediction system used variables susceptibility infection mortality geography weather and patient clinical data to be processed into ANFIS machine learning prediction models and neural networks are several models A classification model that is widely used for hybrid processing in calculating covid 19 infection prediction The datasets that are often used do not fully meet the epidemiological aspects that trigger the spread of COVID 19 infections ANFIS and NN are several classification methods that are widely used for hybrid processing in calculating predictions of the spread of COVID 19 infection

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http://hdl.handle.net/10453/176355