ABSTRACT
COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that, to date, has over 245 million confirmed cases and claimed almost 5 million lives. This disease attacks the respiratory system and comes with a number of symptoms. The US Center for Disease Control and Prevention presents a set of symptoms. However, these symptoms only begin to manifest after a number of days, which prevents early detection of this disease. This absence of symptoms during the early stages is what is considered by many to be the very factor that caused the virus into becoming a pandemic. Nonetheless, symptoms checking has been used in practice by commercial and business establishments as an initial screening for COVID-19. The bothersome process of symptom checking are still in place at the entrances of malls and airports. In this study, we determine whether or not symptom screening is an effective system to be employed to assess individuals for COVID-19. Specifically, it aims to determine whether or not one or a set of symptoms are effective predictors of the RT-PCR test results, the gold standard in Covid-19 testing, using machine learning. Using data from the Philippine Red Cross, classification models are developed using LightGBM, AdaBoost, Gaussian Naïve-Bayes, MultiLayer Perceptron, Quadratic Discriminant Analysis and Decision Tree. These models were evaluated using the following metrics: precision, sensitivity, specificity and the type II error rate. Furthermore, for explainability, symptoms are analyzed as to whether or not they are relatively influential on the predicting whether or not a patient has COVID-19. The high type II error rate, low sensitivity and low relative predictor scores of the most significant predictor symptoms clearly show that symptoms do not correlate with the RT-PCR testing results. Thus, we conclude that symptom screening is not a medically suitable process for determining whether an individual has COVID-19. In fact, it even exposes us to the risk of viral transmission as people congregate at the entrances and lobbies of establishments. © 2022 IEEE.