Time Windows Voting Classifier for COVID-19 Mortality Prediction
GOH, T.; Liu, M.
Show abstract
BackgroundThe ability to predict COVID-19 patients level of severity (death or survival) enables clinicians to prioritise treatment. Recently, using three blood biomarkers, an interpretable machine learning model was developed to predict the mortality of COVID-19 patients. The method was reported to be suffering from performance stability because the identified biomarkers are not consistent predictors over an extended duration. MethodsTo sustain performance, the proposed method partitioned data into three different time windows. For each window, an end-classifier, a mid-classifier and a front-classifier were designed respectively using the XGboost single tree approach. These time window classifiers were integrated into a majority vote classifier and tested with an isolated test data set. ResultsThe voting classifier strengthens the overall performance of 90% cumulative accuracy from a 14 days window to a 21 days prediction window. ConclusionsAn additional 7 days of prediction window can have a considerable impact on a patients chance of survival. This study validated the feasibility of the time window voting classifier and further support the selection of biomarkers features set for the early prognosis of patients with a higher risk of mortality.
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