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Comparing machine learning algorithms for predicting COVID-19 mortality.

Khadijeh Moulaei1, Mostafa Shanbehzadeh2, Zahra Mohammadi-Taghiabad3

  • 1Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.

BMC Medical Informatics and Decision Making
|January 5, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) accurately predicts COVID-19 mortality risk in hospitalized patients. The random forest algorithm demonstrated superior performance, aiding clinical decision-making for high-risk individuals.

Keywords:
Artificial intelligenceCOVID-19CoronavirusMachine learningPrediction hospital mortality

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Public Health

Background:

  • Hospitalized patients with coronavirus disease (COVID-19) face a significant risk of mortality.
  • Machine learning (ML) offers a potential avenue for predicting mortality in COVID-19 patients.

Purpose of the Study:

  • To compare various ML algorithms for predicting COVID-19 mortality.
  • To identify the best-performing ML algorithm for clinical decision support.

Main Methods:

  • Data from 1500 COVID-19 patients (1386 survivors, 144 deaths) were analyzed after feature selection.
  • Multiple ML algorithms were trained to predict mortality using admission data.
  • Model performance was evaluated using metrics derived from the confusion matrix.

Main Results:

  • Dyspnea, ICU admission, and oxygen therapy were top predictors of COVID-19 mortality.
  • The random forest (RF) algorithm achieved 95.03% accuracy, 90.70% sensitivity, and 99.02% ROC.
  • RF outperformed other ML algorithms in predicting patient mortality.

Conclusions:

  • ML models, especially RF, can accurately predict COVID-19 mortality.
  • These predictive tools can help clinicians identify high-risk patients for timely interventions.