Abstract
Medical data can be mined for effective decision making in spread of disease analysis. Globally, Coronavirus (COVID-19) has recently caused highly rated cause of mortality which is a serious threat as the number of coronavirus cases are increasing worldwide. Currently, the techniques of machine learning and predictive analytics has proven importance in data analysis. Predictive analytics techniques can give effective solutions for healthcare related problems and predict the significant information automatically using machine learning models to get knowledge about Covid-19 spread and its trends also. In a nutshell, this chapter aims to discuss upon the latest happenings in the technology front to tackle coronavirus and predict the spread of coronavirus in various cities of Saudi Arabia from purely a dataset perspective, outlines methodologies such as Naïve Bayes and Support vector machine approaches. Also, the chapter briefly covers the performance of the prediction models and provide the prediction results in order to better understand the confirmed, recovered and the mortality cases from COVID-19 infection in KSA regions. It also discusses and highlights the necessity for a Sustainable Healthcare Approach in tackling future pandemics and diseases.
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We acknowledge MoH - KSA data repository for datasets.
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Muniasamy, A., Bhatnagar, R., Karunakaran, G. (2021). Predicting COVID19 Spread in Saudi Arabia Using Artificial Intelligence Techniques—Proposing a Shift Towards a Sustainable Healthcare Approach. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications. Studies in Computational Intelligence, vol 912. Springer, Cham. https://doi.org/10.1007/978-3-030-51920-9_6
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