To investigate the factors related to the severity and mo rtality of COVID-19 using big data-machine learning techniques.
This study included 8070 patients in South Korea diagnosed with COVID-19 between January and July 2020, and whose data were available from the National-Health-Insurance-Service.
Machine-learning algorithms were performed to evaluate the effects of comorbidities on severity and mortality of COVID-19. The most common comorbidities of COVID-19 were pulmonary inflammation followed by hypertension. The model that best predicted severity was a neural network (AUC: 85.06%). The most important variable for predicting severity in the neural network model was a history of influenza (relative importance: 0.083). The model that best predicted mortality was the logistic regression elastic net (EN) model (AUC: 93.86%). The most important variables for mortality in the EN model were age (coefficient: 2.136) and anosmia (coefficient: -1.438).
In COVID-19 patients, influenza was found to be a major adverse factor in addition to old age and male. In addition, anosmia was found to be a major factor associated with lower severity and mortality. Therefore, in the current situation where there is no adequate COVID-19 treatment at present, examining the patient’s history of influenza vaccination and anosmia in addition to age and sex will be an important indicator for predicting the severity and mortality of COVID-19 patients.

Copyright © 2021. Published by Elsevier Ltd.