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Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
- 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.
- Subjects :
- Taste
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
Visual analogue scale
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
lcsh:Medicine
Machine learning
computer.software_genre
Article
taste
03 medical and health sciences
0302 clinical medicine
smell
Medicine
030212 general & internal medicine
030223 otorhinolaryngology
business.industry
SARS-CoV-2
lcsh:R
visual analog scale
COVID-19
General Medicine
Odds ratio
Predictive value
prediction model
machine learning
Taste disorder
Artificial intelligence
business
computer
Subjects
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