COVID-19 screening: use of an artificial neural network

Abstract

ObjectivesCOVID-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.MethodsData 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.ResultsThe 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.ConclusionsHome 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.

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

“…Different models have been used to predict COVID-19 prevalence and mortality rate in recent studies. For example, multiple linear regression [ 8 ], Artificial Neural Network [ 9 ], multilayer perceptron [ 10 ] grey prediction model [ 11 ], simulation model [ 12 ], Holt model [ 13 ], LSTM model [ 14 ], and support vector regression [ 15 , 16 ]. However, the spread of epidemic disease is random and will be affected by many factors [ 17 , 18 ].…”

Classification for Covid-19 Diseases Based on Ensembled Models

“…Narges and Elahe take a step further in ordinary machine learning. They used an artificial neural network approach to build a deep learning model that effectively identifies Covid19 positive patients by collecting clinical symptoms [5]. Although they use a deep learning method, they still base their studies on clinical data from a single disease and a single model.…”