Application of boosted trees to the prognosis prediction of COVID‐19

Abstract

Abstract Background and Aims The precise prediction of COVID‐19 prognosis remains a clinical challenge. In this regard, early identification of severe cases facilitates the triage and management of COVID‐19 cases. The present paper aims to explore the prognosis of COVID‐19 patients based on routine laboratory tests taken when patients are admitted. Methods A data set including 1455 COVID‐19 patients (727 male, 728 female) and their routine laboratory tests conducted upon hospital admission, age, Intensive Care Unit (ICU) admission, and outcome were gathered. The data set was randomly split into the train (75% of the data) and test data set (25% of the data). The explainable boosting machine (EBM) and extreme gradient boosting (XGBoost) were used for predicting the mortality and ICU admission of COVID‐19 cases. Also, feature importance was extracted using EBM and XGBoost. Results The EBM and XGBoost achieved 86.38% and 88.56% accuracy in the test data set, respectively. In addition, EBM and XGBoost predicted the ICU admission with an accuracy of 89.37%, and 79.29% in the test data set for COVID‐19 patients, respectively. Also, obtained models indicated that aspartate transaminase (AST), lymphocyte, blood urea nitrogen (BUN), and age are the most significant predictors of COVID‐19 mortality. Furthermore, the lymphocyte count, AST, and BUN level were the most significant ICU admission predictors of COVID‐19 patients. Conclusions The current study indicated that both EBM and XGBoost could predict the ICU admission and mortality of COVID‐19 cases based on routine hematological and clinical chemistry evaluation at the time of admission. Also, based on the results, AST, lymphocyte count, and BUN levels could be used as early predictors of COVID‐19 prognosis.

Keywords

• COVID‐19
• explainable boosting machine
• machine learning
• prediction
• prognosis
• XGBoost