%0 Journal Article
%A Callejon-Leblic, María A
%A Moreno-Luna, Ramon
%A Del Cuvillo, Alfonso
%A Reyes-Tejero, Isabel M
%A Garcia-Villaran, Miguel A
%A Santos-Peña, Marta
%A Maza-Solano, Juan M
%A Martín-Jimenez, Daniel I
%A Palacios-Garcia, Jose M
%A Fernandez-Velez, Carlos
%A Gonzalez-Garcia, Jaime
%A Sanchez-Calvo, Juan M
%A Solanellas-Soler, Juan
%A Sanchez-Gomez, Serafin
%T Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.
%D 2021
%@ 2077-0383
%U https://hdl.handle.net/10668/27719
%X 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 reverse-transcription 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.
%K COVID-19
%K SARS-CoV-2
%K machine learning
%K prediction model
%K smell
%K taste
%K visual analog scale
%~