Predicting ICU Admission for COVID-19 Patients in Saudi Arabia: A Comparative Study of AdaBoost and Bagging Methods

Abstract

COVID-19's high fatality rate and accurately determining the mortality rate within a particular geographic region continue to be significant concerns. In this study, the authors investigated and assessed the performance of two advanced machine learning approaches, Adaptive Boosting (AdaBoost) and Bootstrap Aggregation (Bagging), as strong predictors of COVID-19-related intensive care unit (ICU) admissions within Saudi Arabia. These models may help Saudi health-care organizations determine who is at a higher risk of readmission, allowing for more targeted interventions and improved patient outcomes. The authors found AdaBoost-RF and Bagging-RF methods produced the most precise models, with accuracy rates of 97.4% and 97.2%, respectively. This work, like prior studies, illustrates the viability of developing, validating, and using machine learning (ML) prediction models to forecast ICU admission in COVID-19 cases. The ML models that have been developed have tremendous potential in the fight against COVID-19 in the health-care industry.