- Academic Editor
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Background: The severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the
right evaluation of viral infection. According to the Centers for Disease Control
and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in
respiratory samples is the gold standard for confirming the disease. However, it
has practical limitations as time-consuming procedures and a high rate of
false-negative results. We aim to assess the accuracy of COVID-19 classifiers
based on Arificial Intelligence (AI) and statistical classification methods
adapted on blood tests and other information routinely collected at the Emergency
Departments (EDs). Methods: Patients admitted to the ED of Careggi
Hospital from April 7th–30th 2020 with pre-specified features of suspected
COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19
likely/unlikely case, based on clinical features and bedside imaging support.
Considering the limits of each method to identify a case of COVID-19, further
evaluation was performed after an independent clinical review of 30-day follow-up
data. Using this as a gold standard, several classifiers were implemented:
Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest
(RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor
(K-NN), Naive Bayes (NB). Results: Most of the classifiers show a ROC