Resumo

PINASCO, Gustavo Carreiro et al. An interpretable machine learning model for COVID-19 screening. J. Hum. Growth Dev. [online]. 2022, vol.32, n.2, pp. 268-274. ISSN 0104-1282.  http://dx.doi.org/10.36311/jhgd.v32.13324.

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

Palavras-chave : COVID-19; machine learning; artificial intelligence; pandemia.

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