An interpretable machine learning model for covid-19 screening

Autores/as

  • 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 dPrefeitura Municipal de Vitória, Brazil.
  • Sheila Cristina de Souza Cruz dPrefeitura 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.
  • Eliane de Fátima Almeida Lima aUniversidade Federal do Espírito Santo – UFES, Brazil;
  • Katia Valéria Manhambusque aUniversidade Federal do Espírito Santo – UFES, Brazil;

DOI:

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

Palabras clave:

COVID-19, machine learning, artificial intelligence, pandemia

Resumen

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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Publicado

2022-06-23

Número

Vol. 32 Núm. 2 (2022)

Sección

ORIGINAL ARTICLES

Licencia

Derechos de autor 2022 Pinasco GC, de Mattos Farina EMJ, Barcellos Filho FN, Fiorotti WF, Ferreira MCM, Souza Cruz SC, Colodette AL, Loureiro LR, Comério T, Farias DCS, Lima EFA, Manhambusque KV

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.