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• W3200601738 abstract "The unprecedented rise in the number of new coronavirus infections worldwide has prompted many researchers to use mathematical and machine-learning-based prediction models to predict future epidemic patterns that will help governments, health service providers, and society understand how to deal with this situation. Using different machine learning methodologies helps researchers to understand the trend curve clearly. These may lead to a better and more effective fight against the epidemic and reduce or end preventive measures, allowing people to return to their everyday lives. This study is based on an analysis of COVID-19 data of KSA. Also, it demonstrates the prediction of the new confirmed cases and death of COVID-19 in the next ten days from 8th July in KSA, which is considered the period of the performing Hajj in 2021. It uses machine learning models such as Support Vector Machine (SVM), Bayesian Edge (BR), Linear Regression (LR), and Moving Average (MA). Each model provides two types of predictions: the number of newly infected cases and deaths over the next 10 days. The results indicate that SVM and MA forecasts have high accuracy, followed by LR which performs well. The BR performs poorly in forecast scenarios when applied with the available data set in forecasting new confirmed cases. All models were accurate in predicting mortality, with the best performing model being SVM, followed by MA, LR, and BR. It also expects an increase in confirmed cases under the SVM model scenario to 511,257 on 17th July from 496,516 on 7th July in the actual daily cumulative cases. The number of deaths will rise to 8,113 on 17th July from 7,921 on 7th July in actual cumulative daily data." @default.
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• W3200601738 date "2021-08-01" @default.
• W3200601738 modified "2023-10-14" @default.
• W3200601738 title "COVID-19 Analysis and Predictions Evaluation for KSA Using Machine Learning" @default.
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• W3200601738 doi "https://doi.org/10.1109/icsecs52883.2021.00054" @default.
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