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Acta Medica Iranica، جلد ۶۰، شماره ۳، صفحات ۱۳۵-۱۴۳
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عنوان انگلیسی Developing an Intelligent System for Diagnosis of COVID-19 Based on Artificial Neural Network
چکیده انگلیسی مقاله 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.
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نویسندگان مقاله | Mostafa Shanbehzadeh
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran


| Raoof Nopour
Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran


| Hadi Kazemi-Arpanahi
Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. AND Department of Health Information Management, Student Research Committee, Abadan University of Medical Sciences, Abadan, Ir.
نشانی اینترنتی https://acta.tums.ac.ir/index.php/acta/article/view/8833
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