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Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost

Mohamed Ezz1,2,*, Murtada K. Elbashir1,3, Hosameldeen Shabana4,5

1 College of Computer and Information Sciences, Jouf University, Sakaka, KSA
2 Faculty of Engineering, Al Azhar University, Cairo, Egypt
3 Faculty of Mathematical and Computer Sciences, University of Gezira, Wad Madani, Sudan
4 Faculty of Medicine, Al Azhar University, Cairo, Egypt
5 College of Medicine, Shaqra University, KSA

* Corresponding Author: Mohamed Ezz. Email: email

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 69(2), 2077-2092. https://doi.org/10.32604/cmc.2021.018155

Abstract

It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited. Delay in ICU admissions is associated with negative outcomes such as mortality and cost. Therefore, early identification of patients with a high risk of respiratory failure can prevent complications, enhance risk stratification, and improve the outcomes of severely-ill hospitalized patients. In this paper, we develop a model that uses the characteristics and information collected at the time of patients’ admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions. We use the data explained and organized in a window-based manner by the Sírio-Libanês hospital team (published on Kaggle). Preprocessing is applied, including imputation, cleaning, and feature selection. In the cleaning process, we remove zero-variance, redundant, and/or highly correlated (measured by the Pearson correlation coefficient) features. We use Extreme Gradient Boosting (XGBoost) with early stopping as a predictor in our developed model. We run the experiment in four stages starting from the features of Window 1 in Stage 1 and then incrementally add the features of Windows 2–4 in Stages 2–4 respectively. We achieve AUCs of 0.73, 0.92, 0.95, and 0.97 in those four stages.

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APA Style
Ezz, M., Elbashir, M.K., Shabana, H. (2021). Predicting the need for ICU admission in COVID-19 patients using xgboost. Computers, Materials & Continua, 69(2), 2077-2092. https://doi.org/10.32604/cmc.2021.018155
Vancouver Style
Ezz M, Elbashir MK, Shabana H. Predicting the need for ICU admission in COVID-19 patients using xgboost. Comput Mater Contin. 2021;69(2):2077-2092 https://doi.org/10.32604/cmc.2021.018155
IEEE Style
M. Ezz, M.K. Elbashir, and H. Shabana "Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost," Comput. Mater. Contin., vol. 69, no. 2, pp. 2077-2092. 2021. https://doi.org/10.32604/cmc.2021.018155



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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