The Selection of Indicators from Initial Blood Routine Test Results to
Improve the Accuracy of Early Prediction of COVID-19 Severity
Abstract
Early prediction of disease severity is important for effective
treatment of COVID-19. We determined that age is a key indicator for
severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of
0.92. In order to improve the accuracy of prediction, we proposed a
Multi Criteria Decision Making (MCDM) algorithm, which combines the
Technique for Order of Preference by Similarity to Ideal Solution
(TOPSIS) and Naïve Bayes (NB) classifier, to further select effective
indicators from patients’ initial blood test results. The MCDM algorithm
selected 3 dominant feature subsets {Age, WBC, LYMC, NEUT}, {Age,
WBC, LMYC} and {Age, NEUT, LYMC}. Using these feature subsets, the
optimized prediction model could achieve an accuracy of 0.82 and an AUC
of 0.93. This result indicated that using age and the indicators
selected by the MCDM algorithm from blood routine test results can
effectively predict the severity of COVID-19 at an early stage.