Prediction of COVID-19 From Hemogram Results and Age Using Machine Learning

Abstract

Introduction: The rapid global dissemination of COVID-19 culminated in the mobilization of great technological efforts aimed at its better understanding and control. In this context, Machine Learning gains notoriety, and its application has been widely documented for pathophysiological, diagnostic, therapeutic, prognostic and monitoring of COVID-19 purposes. The present study aimed to build a model for the prediction of the diagnosis of COVID-19 based on blood count results and age of patients and to identify the main characteristics taken into account by the algorithm for the predictive decision.

Material and Methods: Anonymous data from 1157 patients made available by the COVID-19 Data Sharing / BR repository were used. The work took place in two distinct stages: description and analysis of the data; and construction of the predictive model.

Results: With the exception of hemoglobin measurement, mean corpuscular volume, red cell distribution width, mean platelet volume and neutrophil-lymphocyte ratio, there was a statistically significant association of all other hematological parameters assessed with COVID-19. The predictive model developed from the XGBoost classifier reached an accuracy of 80.0% with a sensitivity of 75.6% and specificity of 82.0%. The variables that had the greatest influence on the predictive decision were basophil, eosinophil and leukocyte measurements. The present study confirms the potential of using blood count results, a widely available and accessible test, in the context of the diagnostic evaluation and pathophysiological investigation of COVID-19.

Conclusion: This work highlights the relevance of the systematization and dissemination of data related to COVID-19 for use in new research.