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

Abstract

Objectives 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°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. Conclusions 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.

Battling COVID-19 using machine learning: A review

“…Logistic regression can be used in binomial, multinomial and ordinal classification. Mohammadi et al, (Mohammadi et al, 2021) used ANN and LR to classify COVID-19 patients in Iran. In (Bhandari et al, 2020), Logistic Regression was used to predict the mortality rate from various blood and clinical parameters.…”

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

“…Similarly COVID-19 early vision diagnosis using MLP was proposed in ( Hammam et al, 2020 ). Also MLP and LR were combined to develop a hybrid model for COVID-19 diagnosis in ( Mohammadi et al, 2021 ). The authors established good performance for the MLP based models in terms of accuracy, sensitivity and specificity.…”

Model Hybrid Nonlinear Regression Logistic (Nlr) –Double Exponensial Smoothing (Des) Dan Penerapannya Pada Jumlah Kasus Kumulatif Covid-19 Di Indonesia Dan Belanda

“…Penelitian mengenai nonlinear regression logistic telah dilakukan oleh (Mohammadi et al 2021) yang meneliti mengenai karakter dari COVID-19 menggunakan nonlinear regression logistic diperoleh bahwa nonlinear regression logistic mempunyai kemampuan yang tinggi dalam melihat perilaku dari COVID-19, selanjutnya (Nusinovici et al 2020) yang menyimpulkan bahwa nonlinear regression logistic merupakan model yang sangat bagus dalam melakukan prediksi terhadap resiko dari penyakit kronis. Selanjut penelitian mengenai double exponensial smoothing telah dilakukan oleh (Guleryuz 2021) dan (AJUNU et al 2020).…”