Preserved in Portico This version is not peer-reviewed
Forecasting the Spread of COVID-19 and ICU Requirements
: Received: 16 March 2021 / Approved: 17 March 2021 / Online: 17 March 2021 (14:56:27 CET)
A peer-reviewed article of this Preprint also exists.
Journal reference: International Journal of Online and Biomedical Engineering (iJOE) 2021, 17
Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.
COVID-19; ICU; feature selection; classification; ARIMA model
MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory
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