COVID-19 screening: use of an artificial neural network.

ABSTRACT

OBJECTIVES: COVID-19 is the biggest pandemic of the 21st century. The disease can be influenced by various sociodemographic factors and can manifest as clinical, pulmonary and gastrointestinal symptoms. This study used an artificial neural network (ANN) model with important sociodemographic factors as well as clinical, pulmonary and gastrointestinal symptoms to screen patients for COVID-19. Patients themselves can screen for these symptoms at home. METHODS: Data on all registered patients were extracted in autumn. The best ANN model was selected from different combinations of connections, some hidden layers and some neurons in each hidden layer. In this study, 70% of the data were used in the network training process and the remaining 30% were used to evaluate the function of the multilayer, feed-forward, back-propagation algorithm. RESULTS: The sensitivity and specificity of the ANN model in diagnosing patients with COVID-19 were 94.5% and 17.4%. In order of priority, clinical symptoms, sociodemographic factors, pulmonary symptoms and gastrointestinal symptoms were important predictive factors for COVID-19 using the ANN model. Screening patients for COVID-19 using clinical symptoms and sociodemographic factors (80% importance) remains essential. CONCLUSIONS: Home monitoring of oxygen saturation and body temperature as well as old age and drug addiction can be helpful in self-screening symptoms of COVID-19 at home, thereby preventing unnecessary visits to medical centres and reducing burden on medical services.