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- W4377081252 abstract "The COVID-19 pandemic is one of the biggest health crises of the twenty-first century, it has completely affected society’s daily life, and has impacted populations worldwide, both economically and socially. The use of machine learning algorithms to study data from the COVID-19 pandemic has been quite frequent in the most varied articles published in recent times. In this paper, we will analyze the impact of several variables (number of cases, temperature, people vaccinated, people fully vaccinated, number of vaccinations, and boosters) on the number of deaths caused by COVID-19 or SARS-CoV-2 in Portugal and find the most appropriate predictive model. Various algorithms were used, such as OLS, Ridge, LASSO, MLP, Gradient Boosting, and Random Forest. The method used for data processing was Cross- Industry Standard Process for Data Mining (CRISP-DM). The data was obtained from an open-access database." @default.
- W4377081252 created "2023-05-20" @default.
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- W4377081252 date "2023-01-01" @default.
- W4377081252 modified "2023-09-27" @default.
- W4377081252 title "Modeling and Predicting Daily COVID-19 (SARS-CoV-2) Mortality in Portugal" @default.
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- W4377081252 doi "https://doi.org/10.1007/978-981-19-9331-2_23" @default.
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