Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Developing an Intelligent System for Diagnosis of COVID-19 Based on Artificial Neural Network

Sun Apr 14 01:12:54 2024

(2022) Developing an Intelligent System for Diagnosis of COVID-19 Based on Artificial Neural Network. Acta Medica Iranica. pp. 135-143. ISSN 00446025 (ISSN)

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An outbreak of atypical pneumonia termed coronavirus disease 2019 (COVID-19) has spread worldwide since the beginning of 2020. It poses a significant threat to the global health and the economy. Physicians face ambiguity in their decision-making for COVID-19 diagnosis and treatment. In this respect, designing an intelligent system for early diagnosis of the disease is critical for mitigating virus spread and resource optimization. This study aimed to establish an artificial neural network (ANNs)-based clinical model to diagnose COVID-19. The retrospective dataset used in this study consisted of 400 COVID-19 case records (250 positives vs. 150 negatives) and 18 columns for the diagnostic features. The backpropagation technique was used to train a neural network. After designing multiple neural network configurations, the area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, and specificity values were calculated to measure the model performance. The two nested loops architecture of 9-10-15-2 (10 and 15 neurons used in layer one and layer two, respectively) with the ROC of 98.2, sensitivity of 96.4, specificity of 90.6, and accuracy of 94 were introduced as the best configuration model for COVID-19 diagnosis. ANN is valuable as a decision-support tool for clinicians to improve the COVID-19 diagnosis. It is promising to implement the ANN model to improve the accuracy and speed of the COVID-19 diagnosis for timely screening, treatment, and careful monitoring. Further studies are warranted for verifying and improving the current ANN model. © 2022 Tehran University of Medical Sciences. All rights reserved.

Item Type: Article
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Artificial neural network Coronavirus disease 2019 (COVID-19) Intelligent system Article back propagation neural network body temperature breathing rate controlled study coronavirus disease 2019 coughing data processing decision support system deep learning diagnostic accuracy diagnostic test diagnostic test accuracy study dyspnea feature selection fever human learning algorithm machine learning major clinical study oxygen saturation positivity rate predictive value quantitative analysis sensitivity and specificity thorax pain weakness
Page Range: pp. 135-143
Journal or Publication Title: Acta Medica Iranica
Journal Index: Scopus
Volume: 60
Number: 3
Identification Number:
ISSN: 00446025 (ISSN)
Depositing User: مهندس مهدی شریفی

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