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Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study

Coronavirus disease 2019 is a fatal viral disease presently sweeping the globe. COVID-19 is a novel coronavirus that produces an infectious illness. Thus, COVID-19 detection in the general population may be helpful. The involvement of machine learning in combating COVID-19 had rapidly increased beca...

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Main Authors: Hasri, Hudzaifah, Mohd. Aris, Siti Armiza, Ahmad, Robiah
Format: Conference or Workshop Item
Published: 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/96638/
http://dx.doi.org/10.1109/NBEC53282.2021.9618763
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spelling my.utm.966382022-08-15T03:48:27Z http://eprints.utm.my/id/eprint/96638/ Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study Hasri, Hudzaifah Mohd. Aris, Siti Armiza Ahmad, Robiah T Technology (General) Coronavirus disease 2019 is a fatal viral disease presently sweeping the globe. COVID-19 is a novel coronavirus that produces an infectious illness. Thus, COVID-19 detection in the general population may be helpful. The involvement of machine learning in combating COVID-19 had rapidly increased because of its efficiency to scale up, faster processing capacity, and more dependable than humans in some healthcare activities. This paper will focus on two models which are Linear Regression (LR) model and Holt's Winter model. The COVID-19 dataset was taken from the Ministry of Health for Malaysia's website. Daily confirmed cases were recorded from 24th of January 2020 to 31st July 2021 and stored in Microsoft Excel. Waikato Environment for Knowledge Analysis (WEKA) software was utilized to perform the prediction of daily cases in the next 14-days and the quality of forecasting models is evaluated by two performance metrics, Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The best model is selected by the lowest value of performance metrics. The comparison shows that the forecasting result of Holt's Winter is more accurate than the LR model. The developed prediction model can help public health officials make better decisions and manage resources to decrease COVID-19 pandemic morbidity and mortality. Therefore, preparation and control procedures can be established. 2021 Conference or Workshop Item PeerReviewed Hasri, Hudzaifah and Mohd. Aris, Siti Armiza and Ahmad, Robiah (2021) Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study. In: 1st National Biomedical Engineering Conference, NBEC 2021, 9 - 10 November 2021, Virtual, Online. http://dx.doi.org/10.1109/NBEC53282.2021.9618763
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Hasri, Hudzaifah
Mohd. Aris, Siti Armiza
Ahmad, Robiah
Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study
description Coronavirus disease 2019 is a fatal viral disease presently sweeping the globe. COVID-19 is a novel coronavirus that produces an infectious illness. Thus, COVID-19 detection in the general population may be helpful. The involvement of machine learning in combating COVID-19 had rapidly increased because of its efficiency to scale up, faster processing capacity, and more dependable than humans in some healthcare activities. This paper will focus on two models which are Linear Regression (LR) model and Holt's Winter model. The COVID-19 dataset was taken from the Ministry of Health for Malaysia's website. Daily confirmed cases were recorded from 24th of January 2020 to 31st July 2021 and stored in Microsoft Excel. Waikato Environment for Knowledge Analysis (WEKA) software was utilized to perform the prediction of daily cases in the next 14-days and the quality of forecasting models is evaluated by two performance metrics, Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The best model is selected by the lowest value of performance metrics. The comparison shows that the forecasting result of Holt's Winter is more accurate than the LR model. The developed prediction model can help public health officials make better decisions and manage resources to decrease COVID-19 pandemic morbidity and mortality. Therefore, preparation and control procedures can be established.
format Conference or Workshop Item
author Hasri, Hudzaifah
Mohd. Aris, Siti Armiza
Ahmad, Robiah
author_facet Hasri, Hudzaifah
Mohd. Aris, Siti Armiza
Ahmad, Robiah
author_sort Hasri, Hudzaifah
title Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study
title_short Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study
title_full Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study
title_fullStr Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study
title_full_unstemmed Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study
title_sort linear regression and holt's winter algorithm in forecasting daily coronavirus disease 2019 cases in malaysia: preliminary study
publishDate 2021
url http://eprints.utm.my/id/eprint/96638/
http://dx.doi.org/10.1109/NBEC53282.2021.9618763
_version_ 1743107007450185728
score 13.222552

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