Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach

COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try t...

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Published in:Journal of Physics: Conference Series
Main Author: Wan Yaacob W.F.; Sobri N.M.; Nasir S.A.M.; Nordin N.I.; Wan Yaacob W.F.; Mukhaiyar U.
Format: Conference paper
Language:English
Published: IOP Publishing Ltd 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120792829&doi=10.1088%2f1742-6596%2f2084%2f1%2f012013&partnerID=40&md5=58e048355c5357c6055ad76f931fb531
id 2-s2.0-85120792829
spelling 2-s2.0-85120792829
Wan Yaacob W.F.; Sobri N.M.; Nasir S.A.M.; Nordin N.I.; Wan Yaacob W.F.; Mukhaiyar U.
Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
2021
Journal of Physics: Conference Series
2084
1
10.1088/1742-6596/2084/1/012013
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120792829&doi=10.1088%2f1742-6596%2f2084%2f1%2f012013&partnerID=40&md5=58e048355c5357c6055ad76f931fb531
COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg's test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
IOP Publishing Ltd
17426588
English
Conference paper
All Open Access; Gold Open Access
author Wan Yaacob W.F.; Sobri N.M.; Nasir S.A.M.; Nordin N.I.; Wan Yaacob W.F.; Mukhaiyar U.
spellingShingle Wan Yaacob W.F.; Sobri N.M.; Nasir S.A.M.; Nordin N.I.; Wan Yaacob W.F.; Mukhaiyar U.
Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
author_facet Wan Yaacob W.F.; Sobri N.M.; Nasir S.A.M.; Nordin N.I.; Wan Yaacob W.F.; Mukhaiyar U.
author_sort Wan Yaacob W.F.; Sobri N.M.; Nasir S.A.M.; Nordin N.I.; Wan Yaacob W.F.; Mukhaiyar U.
title Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
title_short Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
title_full Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
title_fullStr Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
title_full_unstemmed Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
title_sort Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach
publishDate 2021
container_title Journal of Physics: Conference Series
container_volume 2084
container_issue 1
doi_str_mv 10.1088/1742-6596/2084/1/012013
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120792829&doi=10.1088%2f1742-6596%2f2084%2f1%2f012013&partnerID=40&md5=58e048355c5357c6055ad76f931fb531
description COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg's test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
publisher IOP Publishing Ltd
issn 17426588
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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