RT Journal Article
T1 Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.
A1 Callejon-Leblic, María A
A1 Moreno-Luna, Ramon
A1 Del Cuvillo, Alfonso
A1 Reyes-Tejero, Isabel M
A1 Garcia-Villaran, Miguel A
A1 Santos-Peña, Marta
A1 Maza-Solano, Juan M
A1 Martín-Jimenez, Daniel I
A1 Palacios-Garcia, Jose M
A1 Fernandez-Velez, Carlos
A1 Gonzalez-Garcia, Jaime
A1 Sanchez-Calvo, Juan M
A1 Solanellas-Soler, Juan
A1 Sanchez-Gomez, Serafin
K1 COVID-19
K1 SARS-CoV-2
K1 machine learning
K1 prediction model
K1 smell
K1 taste
K1 visual analog scale
AB 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.
SN 2077-0383
YR 2021
FD 2021-02-03
LK https://hdl.handle.net/10668/27719
UL https://hdl.handle.net/10668/27719
LA en
DS RISalud
RD Apr 8, 2025