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Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study

Fernanda Sumika Hojo De Souza1, Natália Satchiko Hojo-Souza2, Edimilson Batista Dos Santos1

  • 1Department of Computer Science, Federal University of São João Del-Rei, São João Del-Rei, Brazil.

Frontiers in Artificial Intelligence
|September 13, 2021
PubMed

Insights

Machine learning accurately predicts COVID-19 patient outcomes using demographic and clinical data. This aids physicians in decision-making, improving response times and preventing healthcare system overload.

Area of Science:

  • Medical Informatics
  • Public Health
  • Machine Learning in Healthcare

Background:

  • Brazil registered its first COVID-19 case in February 2020, rapidly escalating to over 672,000 cases and 36,000 deaths by June 2020.
  • Accurate diagnosis and prognosis are crucial for effective COVID-19 patient treatment and healthcare system management.

Purpose of the Study:

  • To develop and evaluate a machine learning model for predicting poor prognosis and outcomes in COVID-19 patients.
  • To identify key patient characteristics influencing disease severity and predict patient outcomes.

Main Methods:

  • Utilized a dataset of 8,443 COVID-19 patients with demographic, clinical, and comorbidity information.
  • Applied machine learning algorithms to predict disease outcomes (cure or death).

Main Results:

  • The best prediction model achieved a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.92.
  • The model demonstrated high performance with a Sensitivity of 0.88 and Specificity of 0.82.

Conclusions:

  • Machine learning models, integrating patient data, can effectively assist in prognostic prediction for COVID-19.
  • This approach supports physician decision-making, enabling faster interventions and mitigating healthcare system strain.

Keywords:
BrazilCOVID-19disease outcomemachine learningprediction model

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