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A comparison of machine learning algorithms in predicting COVID-19 prognostics.

Serpil Ustebay1, Abdurrahman Sarmis2, Gulsum Kubra Kaya3,4

  • 1Department of Computer Engineering, Istanbul Medeniyet University, Istanbul, Turkey.

Internal and Emergency Medicine
|September 18, 2022
PubMed
Summary

Machine learning models accurately predict COVID-19 patient outcomes, including intensive care needs and mortality risk. Tree-based algorithms like Extra Tree and CatBoost showed superior performance in these prognostic predictions.

Keywords:
COVID-19Infectious diseasesMachine learningPrognostic predictionsRisk factors

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Area of Science:

  • Computational biology
  • Medical informatics
  • Machine learning in healthcare

Background:

  • Machine learning (ML) is crucial for developing prognostic and diagnostic models to aid clinical decision-making.
  • Predicting intensive care needs, intubation, and mortality risk in COVID-19 patients is vital for resource allocation and patient management.

Purpose of the Study:

  • To evaluate eight supervised ML algorithms for predicting critical outcomes in COVID-19 patients.
  • To identify key features influencing prognostic predictions.
  • To compare the performance of different ML algorithms in COVID-19 prognosis.

Main Methods:

  • Utilized two datasets: one with demographics and clinical data (n=11,712), and another including blood test results (n=602).
  • Developed and compared eight supervised ML algorithms, including Extra Tree and CatBoost classifiers.
  • Assessed model performance using Area Under the Receiver Operating Characteristic Curve (AUROC).

Main Results:

  • All prognostic models achieved an AUROC exceeding 0.92.
  • Extra Tree and CatBoost classifiers demonstrated superior performance with AUROC values over 0.94.
  • Key predictive features identified include C-reactive protein, lymphocyte ratio, lactic acid, and serum calcium levels.

Conclusions:

  • Supervised ML, particularly tree-based algorithms, offers significant value in predicting COVID-19 prognosis.
  • Accurate prognostic models can support clinical decision-making and improve patient outcomes.
  • Biomarkers like C-reactive protein and blood cell counts are critical indicators for COVID-19 severity.