COVID-19 diagnosis by routine blood tests using machine learning

TL;DR

This study developed a machine learning model using routine blood tests to diagnose COVID-19 with high accuracy, providing a rapid, accessible alternative or complement to RT-PCR and imaging.

Contribution

The paper introduces a novel machine learning diagnostic tool based on routine blood parameters, achieving high accuracy for COVID-19 detection.

Findings

AUC of 0.97 for COVID-19 diagnosis

Key blood parameters identified include MCHC, eosinophil count, albumin, INR, and prothrombin activity

Model sensitivity of 81.9% and specificity of 97.9%

Abstract

Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.