Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

Background: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19. Methods: A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19. Results: In 750 confirmed patients, Common symptoms were: fever (%) >37.5 degrees C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model. Conclusion: The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection.

Automated summary

In this research, a comparative study was conducted on COVID-19 patients in 6 provinces of Iran, revealing similar prevalent symptoms and underlying diseases but significant differences in symptom incidence among patients. Furthermore, the ANN and LR models demonstrated high accuracy in diagnosing COVID-19 infection.