Document Type : Original Article


1 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran

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

3 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran, Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran


BACKGROUND: From December 2019, atypical pneumonia termed COVID‑19 has been increasing
exponentially across the world. It poses a great threat and challenge to world health and the economy.
Medical specialists face uncertainty in making decisions based on their judgment for COVID‑19.
Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs)
for diagnosing COVID‑19.
MATERIALS AND METHODS: Using a single‑center registry, we studied the records of 250 confirmed
COVID‑19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation
coefficient technique was used to determine the most significant variables of the ANN model. The
variables at P < 0.05 were used for model construction. We applied the back‑propagation technique
for training a neural network on the dataset. After comparing different neural network configurations,
the best configuration of ANN was acquired, then its strength has been evaluated.
RESULTS: After the feature selection process, a total of 18 variables were determined as the most
relevant predictors for developing the ANN models. The results indicated that two nested loops’
architecture of 9‑10‑15‑2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area
under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was
introduced as the best configuration model for COVID‑19 diagnosis.
CONCLUSION: The proposed ANN‑based clinical decision support system could be considered as a
suitable computational technique for the frontline practitioner in early detection, effective intervention,
and possibly a reduction of mortality in patients with COVID‑19.


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