Article
Loss of smell and taste can accurately predict COVID-19 infection: a machine-learning approach
Author/s | Callejón-Leblic, María A.
Moreno-Luna, Ramón Cuvillo, Alfonso del Reyes-Tejero, Isabel M. García-Villarán, Miguel Á. Santos-Peña, Marta Maza Solano, Juan Manuel Solanellas Soler, Juan Sánchez Gómez, Serafín |
Department | Universidad de Sevilla. Departamento de Cirugía |
Publication Date | 2021 |
Deposit Date | 2022-09-23 |
Published in |
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Abstract | The COVID-19 outbreak has spread extensively around the world. Loss of smell and
taste have emerged as main predictors for COVID-19. The objective of our study is to develop a
comprehensive machine learning (ML) modelling ... The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reversetranscription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction. |
Project ID. | PAIDI2020 |
Citation | Callejón-Leblic, M.A., Moreno-Luna, R., Cuvillo, A.d., Reyes-Tejero, I.M., García-Villarán, M.Á., Santos-Peña, M.,...,Sánchez Gómez, S. (2021). Loss of smell and taste can accurately predict COVID-19 infection: a machine-learning approach. Journal of Clinical Medicine, 10 (4) |
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