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Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

Authors :
Daniel I Martín-Jimenez
Alfonso Del Cuvillo
Juan Manuel Maza-Solano
José María Palacios-García
M. A. Callejón-Leblic
Jaime González-García
Carlos Fernandez-Velez
Marta Santos-Peña
Juan M Sanchez-Calvo
Ramón Moreno-Luna
Miguel A Garcia-Villaran
Serafín Sánchez-Gómez
Juan Solanellas-Soler
Isabel M Reyes-Tejero
Source :
Journal of Clinical Medicine, Volume 10, Issue 4, Journal of Clinical Medicine, Vol 10, Iss 570, p 570 (2021)
Publication Year :
2021
Publisher :
Multidisciplinary Digital Publishing Institute, 2021.

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 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.

Details

Language :
English
ISSN :
20770383
Database :
OpenAIRE
Journal :
Journal of Clinical Medicine
Accession number :
edsair.doi.dedup.....42e5d32adaf71433d3c9b2eea1ca4756
Full Text :
https://doi.org/10.3390/jcm10040570