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), Naïve Bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann-Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had an accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had an accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients' hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness, and decongest health facilities in Zambia and other countries with similar settings.

Figure 1

Visual research conceptual framework.

Figure 2

Machine learning modelling steps.

Figure 3

Proportion of patients who recovered and patients who died.

Figure 4

Proportions of two mortality classes. (a) Imbalanced mortality classes. (b) Classes balanced by SMOTE.

Figure 5

Feature importance analysis. (a) Mutual information scores. (b) Multisurf scores.

Figure 6

Normalized compound feature importance plot.

Figure 7

ROC_AUC of models for selected features. (a) For imbalanced classes. (b) For balanced classes.

Figure 8

PRC_AUC for selected features. (a) Imbalanced classes. (b) Balanced classes.

Figure 9

ROC_AUC for balanced classes. (a) All features. (b) Selected features.