Forecasting for vaccinated COVID-19 cases using supervised machine learning in healthcare sector

Mohammad Khraisat, Ali Khalaf and Abd Ghani, Mohd Khanapi (2025) Forecasting for vaccinated COVID-19 cases using supervised machine learning in healthcare sector. Journal of Intelligent Systems and Internet of Things, 14 (2). 165 - 177. ISSN 2769-786X

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Abstract

Machine learning (ML)-based forecasting techniques have demonstrated significant value in predicting postoperative outcomes, aiding in improved decision-making for future tasks. ML algorithms have already been applied in various fields where identifying and ranking risk variables are essential. To address forecasting challenges, a wide range of predictive techniques is commonly employed. Research indicates that ML-based models can accurately predict the impact of COVID-19 on Jordan's healthcare system, a concern now recognized as a potential global health threat. Specifically, to determine COVID-19 risk classifications, this study utilized three widely adopted forecasting models: support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and linear regression (LR). The findings reveal that applying these techniques in the current COVID-19 outbreak scenario is a viable approach. Results indicate that LR outperforms all other models tested in accurately forecasting death rates, recovery rates, and newly reported cases, with LASSO following closely. However, based on the available data, SVM exhibits lower performance across all predictive scenarios.

Item Type:	Article
Uncontrolled Keywords:	COVID-19, Supervised machine learning, Future forecasting, Least absolute shrinkage and selection operator, Support vector machine
Divisions:	Faculty of Information and Communication Technology > Department of Software Engineeering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 May 2026 02:06
Last Modified:	13 May 2026 02:06
URI:	http://eprints.utem.edu.my/id/eprint/29717
Statistic Details:	View Download Statistic

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