An interpretable machine learning model for covid-19 screening

Authors

  • Gustavo Carreiro Pinasco Universidade Federal do Espírito Santo – UFES, Brazil;
  • Eduardo Moreno Júdice de Mattos Farina Universidade Federal de São Paulo – UNIFESP, Brazil;
  • Fabiano Novaes Barcellos Filho cEscola Superior de Ciências da Santa Casa de Misericórdia de Vitória – EMESCAM, Brazil;
  • Willer França Fiorotti cEscola Superior de Ciências da Santa Casa de Misericórdia de Vitória – EMESCAM, Brazil;
  • Matheus Coradini Mariano Ferreira Prefeitura Municipal de Vitória, Brazil.
  • Sheila Cristina de Souza Cruz Prefeitura Municipal de Vitória, Brazil.
  • Andre Louzada Colodette cEscola Superior de Ciências da Santa Casa de Misericórdia de Vitória – EMESCAM, Brazil;
  • Luciene Rossati Loureiro dPrefeitura Municipal de Vitória, Brazil.
  • Tatiane Comério dPrefeitura Municipal de Vitória, Brazil.
  • Dilzilene Cunha Sivirino Farias dPrefeitura Municipal de Vitória, Brazil.
  • Katia Valéria Manhambusque aUniversidade Federal do Espírito Santo – UFES, Brazil;
  • Eliane de Fátima Almeida Lima aUniversidade Federal do Espírito Santo – UFES, Brazil;

DOI:

https://doi.org/10.36311/jhgd.v32.13324

Keywords:

COVID-19, machine learning, artificial intelligence, pandemia

Abstract

Introduction: the Coronavirus Disease 2019 (COVID-19) is a viral disease which has been declared a pandemic by the WHO. Diagnostic tests are expensive and are not always available. Researches using machine learning (ML) approach for diagnosing SARS-CoV-2 infection have been proposed in the literature to reduce cost and allow better control of the pandemic.

Objective: we aim to develop a machine learning model to predict if a patient has COVID-19 with epidemiological data and clinical features.

Methods: we used six ML algorithms for COVID-19 screening through diagnostic prediction and did an interpretative analysis using SHAP models and feature importances.

Results: our best model was XGBoost (XGB) which obtained an area under the ROC curve of 0.752, a sensitivity of 90%, a specificity of 40%, a positive predictive value (PPV) of 42.16%, and a negative predictive value (NPV) of 91.0%. The best predictors were fever, cough, history of international travel less than 14 days ago, male gender, and nasal congestion, respectively.

Conclusion: We conclude that ML is an important tool for screening with high sensitivity, compared to rapid tests, and can be used to empower clinical precision in COVID-19, a disease in which symptoms are very unspecific.

 

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Published

2022-06-23

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ORIGINAL ARTICLES