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

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 w...

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Veröffentlicht in:Journal of clinical medicine 2021-02, Vol.10 (4), p.570
Hauptverfasser: Callejon-Leblic, María A, Moreno-Luna, Ramon, Del Cuvillo, Alfonso, Reyes-Tejero, Isabel M, Garcia-Villaran, Miguel A, Santos-Peña, Marta, Maza-Solano, Juan M, Martín-Jimenez, Daniel I, Palacios-Garcia, Jose M, Fernandez-Velez, Carlos, Gonzalez-Garcia, Jaime, Sanchez-Calvo, Juan M, Solanellas-Soler, Juan, Sanchez-Gomez, Serafin
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Sprache:eng
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Zusammenfassung: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.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm10040570