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COVID-19 diagnosis by routine blood tests using machine learning.

Matjaž Kukar1,2, Gregor Gunčar1,3, Tomaž Vovko4

  • 1Smart Blood Analytics Swiss SA, Höschgasse 25, 8008, Zurich, Switzerland.

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|May 25, 2021
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Summary

A machine learning model using routine blood tests aids COVID-19 diagnosis. This tool, with 81.9% sensitivity and 97.9% specificity, assists physicians in identifying potential cases for further RT-PCR confirmation.

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Area of Science:

  • Medical diagnostics
  • Machine learning in healthcare
  • Hematology

Background:

  • Routine blood parameters change in COVID-19 patients, complicating diagnosis.
  • Existing diagnostic methods like RT-PCR and chest CT have limitations.
  • Need for accessible and rapid diagnostic tools for COVID-19.

Purpose of the Study:

  • To develop and validate a machine learning model for COVID-19 diagnosis using routine blood tests.
  • To identify key blood parameters indicative of COVID-19.
  • To assess the diagnostic performance of the model.

Main Methods:

  • Constructed a machine learning model trained on blood tests from 5333 patients with various infections and 160 COVID-19 patients.
  • Utilized XGBoost algorithm for feature importance scoring.
  • Employed t-SNE visualization to analyze blood parameter patterns.

Main Results:

  • Achieved a cross-validated AUC of 0.97 with 81.9% sensitivity and 97.9% specificity.
  • Identified MCHC, eosinophil count, albumin, INR, and prothrombin activity as key diagnostic parameters.
  • Observed that severe COVID-19 blood profiles resemble bacterial infections more than viral ones.

Conclusions:

  • The machine learning model demonstrates high accuracy for COVID-19 diagnosis, comparable to RT-PCR and CT scans.
  • Routine blood tests can serve as a valuable tool for initial COVID-19 screening.
  • This approach can complement existing diagnostic methods, improving patient management.