Prediction of COVID-19 Severity and Mortality in Hospitalized Children Using Machine Learning Tree-based Classifiers

Abstract

Background Children make up a large percentage of Coronavirus Disease 2019 (COVID-19) hospital admissions, but there is little information available about the features to predict the severity status of the illness or mortality in pediatrics. Logistic regression, supporting vector machine and ensemble machine learning algorithms were used to develop predictive models and identify prognostic factors for severity and mortality of COVID-19 in hospitalized children. Methods A total of 183 children with COVID-19 under the age of 18 years hospitalized in a referral hospital in Yazd province, Iran, from March 1, 2020 to August 1, 2021 were considered for this study. Logistic regression, and machine learning classifiers including supporting vector machine, decision tree, random forest, Bagging classifier trees, Gradient boosted decision trees, and Adaptive boost classifier trees were employed to predict the development of mild/severe or critical COVID-19 and death occurrence during hospitalization. Each model performance was assessed through five-fold cross-validation method, with evaluation metrics and area under the curve. In addition, the best clinical predictive models were used to identify significant factors between severe and non-severe groups, as well as between survivors and non-survivors. Results Seven predictive models were developed using the medical files of 183 hospitalized children, consisting of 94 and 89 (48.6%) in non-severe and severe groups, respectively, as well as 159 survivors and 24 (13%) non-survivors. In prediction of severity status, both decision tree and random forest algorithms had the highest accuracy of 73.3% and 68.7% to predict severity status in balanced data, respectively. Based on decision tree, respiratory distress and cough at the time of admission could be regarded as the as the key factors to estimate the likelihood of severity status. The results also showed that Gradient boosted decision trees, and Adaptive boost classifier trees had the best performance for mortality prediction in balanced data considering the accuracy of 88.8% and 87.7%, respectively. Cough at the time of admission, age group of 1–13 years old, and non-normal WBC could be considered as predictive factors for death occurrence. Conclusions This study indicated that tree-based classifiers were the best machine learning approaches for predicting severity status and mortality in hospitalized children with COVID-19. Clinical symptoms at the time of admission identified as the most predictive features though optimal algorithms.