Author/Editor | Kukar, Matjaž; Gunčar, Gregor; Vovko, Tomaž; Podnar, Simon; Černelč, Peter; Brvar, Miran; Zalaznik, Mateja; Notar, Mateja; Moškon, Sašo; Notar, Marko | |
Title | COVID-19 diagnosis by routine blood tests using machine learning | |
Type | članek | |
Vol. and No. | Letnik 11, št. 1 | |
Publication year | 2021 | |
Volume | str. 1-9 | |
ISSN | 2045-2322 - Scientific reports | |
Language | eng | |
Abstract | Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19- positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID- 19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a 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 represent a significant contribution to improvements in COVID- 19 diagnosis. | |
Keywords | COVID-19 blood tests machine learning COVID-19 krvne preiskave strojno učenje |