1. Loss of smell and taste can accurately predict COVID-19 infection: a machine-learning approach
- Author
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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, and Universidad de Sevilla. Departamento de Cirugía
- Subjects
Smell ,Prediction model ,SARS-CoV-2 ,Taste ,Machine learning ,COVID-19 ,Visual analog scale - 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 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. Junta de Andalucía
- Published
- 2021