Callejon-Leblic, María AMoreno-Luna, RamonDel Cuvillo, AlfonsoReyes-Tejero, Isabel MGarcia-Villaran, Miguel ASantos-Peña, MartaMaza-Solano, Juan MMartín-Jimenez, Daniel IPalacios-Garcia, Jose MFernandez-Velez, CarlosGonzalez-Garcia, JaimeSanchez-Calvo, Juan MSolanellas-Soler, JuanSanchez-Gomez, Serafin2025-01-072025-01-072021-02-032077-0383https://hdl.handle.net/10668/27719The 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/COVID-19SARS-CoV-2machine learningprediction modelsmelltastevisual analog scaleresearch article33546319open access10.3390/jcm10040570PMC7913595https://www.mdpi.com/2077-0383/10/4/570/pdf?version=1612408135https://pmc.ncbi.nlm.nih.gov/articles/PMC7913595/pdf