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- W4386804906 abstract "Statistical methods and models are the backbone of machine learning, which allows computers to autonomously discover and exploit data patterns. The ultimate objective is for computers to accurately forecast outcomes based on historical data. In this work, we present two related research projects: one on stacking and the other on boosting, two ensemble learning approaches. This research investigates its behavior from several angles and sheds light on its relationship to related ensemble learning schemes by showing that it is typically the best option and that stacking can be used to replicate the vast majority of ensemble learning schemes. The COVID-19 clinical dataset has been used to test these strategies for use in anticipating and preparing healthcare systems to avert collapse, where collapse is defined as an over-capacity demand of ICU beds. F1-measures stacking is superior to other methods in terms of accuracy, precision, and recall, as shown by the findings." @default.
- W4386804906 created "2023-09-17" @default.
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- W4386804906 date "2023-01-01" @default.
- W4386804906 modified "2023-09-27" @default.
- W4386804906 title "Prognostic Stacking Machine Learning Model for Intensive Care Unit Admission Prediction of COVID Patients" @default.
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- W4386804906 doi "https://doi.org/10.1007/978-981-99-3716-5_42" @default.
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