2023
DOI: 10.1155/2023/8921220
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Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning

Abstract: The coronavirus disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), N… Show more

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Cited by 2 publications
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References 37 publications
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“…It leverages abundant data and features to construct intricate nonlinear models, capturing risk factors and potential interactions contributing to the development of diseases. By integrating multifaceted data, including clinical data, radiographic data, and laboratory data, the XGBoost algorithm can establish predictive models that offer physicians accurate judgement and decision support, thereby enhancing therapeutic outcomes and mitigating adverse reactions [ 16 , 17 ].…”
Section: Discussionmentioning
confidence: 99%
“…It leverages abundant data and features to construct intricate nonlinear models, capturing risk factors and potential interactions contributing to the development of diseases. By integrating multifaceted data, including clinical data, radiographic data, and laboratory data, the XGBoost algorithm can establish predictive models that offer physicians accurate judgement and decision support, thereby enhancing therapeutic outcomes and mitigating adverse reactions [ 16 , 17 ].…”
Section: Discussionmentioning
confidence: 99%