Document Type : Original Article
Authors
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
Abstract
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.
Keywords
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in critically‑ill patients with COVID‑19: Rationale and design
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et al. World Health Organization declares global emergency:
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2020;76:71‑6.
6. Koh KW. Physical activity guideline for social distancing during
COVID‑19. Korean J Health Educ Promot 2020; 37(1):109-112.
7. Bains VK, Bains R, Gupta V, Salaria SK. Knowledge of COVID‑19
and its implications in dental treatment, and practices of
personal protective equipment among dentists: A survey‑based
assessment. J Edu Health Promot 2021;10:79.
8. Jacobsen KH. Will COVID‑19 generate global preparedness?
Lancet 2020;395:1013‑4.
9. Wang P, Zheng X, Li J, Zhu B. Prediction of epidemic trends in
COVID‑19 with logistic model and machine learning technics.
Chaos Solitons Fractals 2020;139:110058.
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A previously unknown SARS‑CoV‑2 virus of pandemic potential
infecting humans – Call for a One Health approach. One Health
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11. Torrealba‑Rodriguez O , C o n d e ‑ G u t i é r r e z R A ,
Hernández‑Javier AL. Modeling and prediction of COVID‑19 in
Mexico applying mathematical and computational models. Chaos
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risk assessment decision support system for general practitioners:
Design and development study. JMed Internet Res 2020;22:e19786.
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MTNet: COVID‑19 detection with multi‑task deep learning
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14. Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A.
Utility of artificial intelligence amidst the COVID 19 pandemic:
A review. J Med Syst 2020;44:156.
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respiratory syndrome coronavirus 2 (SARS‑CoV‑2) and
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resolution mass spectrometry after data independent acquisition
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modeling of novel coronavirus (COVID‑19) incidence rates across
the continental United States. Int J Environ Res Public Health
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COVID‑19 X‑ray dataset: A novel detection model based on GAN
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Health 2020;17:3730.
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Predicting the dynamics of the COVID‑19 pandemic in the united
states using graph theory‑based neural networks. Int J Environ
Res Public Health 2021 Apr 6;18(7):3834.
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findings. Appl Soft Comput 2021;106:107329.
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covid‑19 in Saudi Arabia. Comput Mater Continua 2021;66:2787‑96.
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network with prey predator algorithm for prediction of the
COVID‑19: The case of Brazil and Mexico. Mathematics 2021;9:1‑4.
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modeling of novel coronavirus (COVID‑19) incidence rates across
the continental United States. Int J Environ Res Public Health
2020;17:1‑3.
36. Pal R, Sekh AA, Kar S, Prasad DK. Neural network based country
wise risk prediction of COVID‑19. Appl Sci (Switzerland)
2020;10(18): 6448.
37. Bassi PRAS, Attux R. A deep convolutional neural network
for COVID‑19 detection using chest X‑rays. Res Biomed Eng
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Automatic COVID‑19 detection from X‑ray images using
ensemble learning with convolutional neural network. Pattern
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et al. Coronavirus (COVID‑19) detection from chest radiology
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Neural Network Architectures for Prediction of COVID‑19
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quicker through artificial intelligence framework using a mobile
phone‑based survey when cities and towns are under quarantine.
Infect Control Hosp Epidemiol 2020;41:826‑30.
2. Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, et al. A familial
cluster of pneumonia associated with the 2019 novel coronavirus
indicating person‑to‑person transmission: A study of a family
cluster. Lancet 2020;395:514‑23.
3. BikdeliB, TalasazAH, RashidiF, Sharif‑KashaniB, FarrokhpourM,
Bakhshandeh H, et al. Intermediate versus standard‑dose
prophylactic anticoagulation and statin therapy versus placebo
in critically‑ill patients with COVID‑19: Rationale and design
of the INSPIRATION/INSPIRATION‑S studies. Thromb Res
2020;196:382‑94.
4. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R.
Features, evaluation and treatment coronavirus (COVID‑19). In:
Statpearls. Treasure Island (FL): StatPearls Publishing; 2020.
5. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al‑Jabir A,
et al. World Health Organization declares global emergency:
A review of the 2019 novel coronavirus (COVID‑19). Int J Surg
2020;76:71‑6.
6. Koh KW. Physical activity guideline for social distancing during
COVID‑19. Korean J Health Educ Promot 2020; 37(1):109-112.
7. Bains VK, Bains R, Gupta V, Salaria SK. Knowledge of COVID‑19
and its implications in dental treatment, and practices of
personal protective equipment among dentists: A survey‑based
assessment. J Edu Health Promot 2021;10:79.
8. Jacobsen KH. Will COVID‑19 generate global preparedness?
Lancet 2020;395:1013‑4.
9. Wang P, Zheng X, Li J, Zhu B. Prediction of epidemic trends in
COVID‑19 with logistic model and machine learning technics.
Chaos Solitons Fractals 2020;139:110058.
10. El Zowalaty ME, Järhult JD. From SARS to COVID‑19:
A previously unknown SARS‑CoV‑2 virus of pandemic potential
infecting humans – Call for a One Health approach. One Health
2020;9:100124.
11. Torrealba‑Rodriguez O , C o n d e ‑ G u t i é r r e z R A ,
Hernández‑Javier AL. Modeling and prediction of COVID‑19 in
Mexico applying mathematical and computational models. Chaos
Solitons Fractals 2020;138:109946.
12. Liu Y, Wang Z, Ren J, Tian Y, Zhou M, Zhou T, et al. A COVID‑19
risk assessment decision support system for general practitioners:
Design and development study. JMed Internet Res 2020;22:e19786.
13. Alom MZ, Rahman M, Nasrin MS, Taha TM, Asari VK. COVID_
MTNet: COVID‑19 detection with multi‑task deep learning
approaches. arXiv 2020;7(1):35-46.
14. Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A.
Utility of artificial intelligence amidst the COVID 19 pandemic:
A review. J Med Syst 2020;44:156.
15. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute
respiratory syndrome coronavirus 2 (SARS‑CoV‑2) and
coronavirus disease‑2019 (COVID‑19): The epidemic and the
challenges. Int J Antimicrob Agents 2020;55:105924.
16. Hussain A, Bhowmik B, do Vale Moreira NC. COVID‑19 and
diabetes: Knowledge in progress. Diabetes Res Clin Pract
2020;162:108142.
17. Moujaess E, Kourie HR, Ghosn M. Cancer patients and research
during COVID‑19 pandemic: A systematic review of current
evidence. Crit Rev Oncol Hematol 2020;150:102972.
18. Harahwa TA, Lai Yau TH, Lim‑Cooke MS, Al‑Haddi S, Zeinah M,
Harky A. The optimal diagnostic methods for COVID‑19.
Diagnosis (Berl) 2020;7:349‑56.
19. Wu SY, Yau HS, Yu MY, Tsang HF, Chan LW, Cho WCS, et al.
The diagnostic methods in the COVID‑19 pandemic, today and
in the future. Expert Rev Mol Diagn 2020;20:985‑93.
20. Hassanien AE, Salama A, Darwsih A. Artificial intelligence
approach to predict the COVID‑19 patient’s recovery. EasyChair
2020;3223:12-25.
21. Jin C, Chen W, Cao Y, Xu Z, Zhang X, Deng L, et al. Development
and evaluation of an AI system for COVID‑19 diagnosis. medRxiv
2020;11:1-14.
22. Sipior JC. Considerations for development and use of AI in
response to COVID‑19. Int J Inf Manage 2020;55:102170.
23. Wong ZS, Zhou J, Zhang Q. Artificial Intelligence for infectious
disease Big Data Analytics. Infect Dis Health 2019;24:44‑8.
24. Streun GL, Elmiger MP, Dobay A, Ebert L, Kraemer T. A machine
learning approach for handling big data produced by high
resolution mass spectrometry after data independent acquisition
of small molecules – Proof of concept study using an artificial
neural network for sample classification. Drug Test Anal
2020;12:836‑45.
25. Yang H, Zhang Z, Zhang J, Zeng XC. Machine learning and
artificial neural network prediction of interfacial thermal
resistance between graphene and hexagonal boron nitride.
Nanoscale 2018;10:19092‑9.
26. Yoo TK, Kim DW, Choi SB, Oh E, Park JS. Simple scoring
system and artificial neural network for knee osteoarthritis risk
prediction: A cross‑sectional study. PLoS One 2016;11:e0148724.
27. Mollalo A, Rivera KM, Vahedi B. Artificial neural network
modeling of novel coronavirus (COVID‑19) incidence rates across
the continental United States. Int J Environ Res Public Health
2020;17:4204.
28. Loey M, Smarandache F, M Khalifa NE. Within the lack of chest
COVID‑19 X‑ray dataset: A novel detection model based on GAN
and deep transfer learning. Symmetry 2020;12:651.
29. Rong D, Xie L, Ying Y. Computer vision detection of foreign
objects in walnuts using deep learning. Comput Electron Agric
2019;162:1001‑10.
30. Shaffiee Haghshenas S, Pirouz B, Shaffiee Haghshenas S, Pirouz B,
Piro P, Na KS, et al. Prioritizing and analyzing the role of climate
and urban parameters in the confirmed cases of COVID‑19 based
on artificial intelligence applications. Int J Environ Res Public
Health 2020;17:3730.
31. Davahli MR, Fiok K, Karwowski W, Aljuaid AM, Taiar R.
Predicting the dynamics of the COVID‑19 pandemic in the united
states using graph theory‑based neural networks. Int J Environ
Res Public Health 2021 Apr 6;18(7):3834.
32. Göreke V, Sarı V, Kockanat S. A novel classifier architecture based on deep neural network for COVID‑19 detection using laboratory
findings. Appl Soft Comput 2021;106:107329.
33. Hamadneh NN, Khan WA, Ashraf W, Atawneh SH, Khan I,
Hamadneh BN. Artificial neural networks for prediction of
covid‑19 in Saudi Arabia. Comput Mater Continua 2021;66:2787‑96.
34. Hamadneh NN, Tahir M, Khan WA. Using artificial neural
network with prey predator algorithm for prediction of the
COVID‑19: The case of Brazil and Mexico. Mathematics 2021;9:1‑4.
35. Mollalo A, Rivera KM, Vahedi B. Artificial neural network
modeling of novel coronavirus (COVID‑19) incidence rates across
the continental United States. Int J Environ Res Public Health
2020;17:1‑3.
36. Pal R, Sekh AA, Kar S, Prasad DK. Neural network based country
wise risk prediction of COVID‑19. Appl Sci (Switzerland)
2020;10(18): 6448.
37. Bassi PRAS, Attux R. A deep convolutional neural network
for COVID‑19 detection using chest X‑rays. Res Biomed Eng
20211;2:1-10.
38. Das AK, Ghosh S, Thunder S, Dutta R, Agarwal S, Chakrabarti A.
Automatic COVID‑19 detection from X‑ray images using
ensemble learning with convolutional neural network. Pattern
Anal Appl 2021;24:1-14.
39. Gilanie G, Bajwa UI, Waraich MM, Asghar M, Kousar R, Kashif A,
et al. Coronavirus (COVID‑19) detection from chest radiology
images using convolutional neural networks. Biomed Signal
Process Control 2021;66:102490.
40. Liang S, Liu H, Gu Y, Guo X, Li H, Li L, et al. Fast automated
detection of COVID‑19 from medical images using convolutional
neural networks. Commun Biol 2021;4:35.
41. Maior CB, Santana JM, Lins ID, Moura MJ. Convolutional
neural network model based on radiological images to support
COVID‑19 diagnosis: Evaluating database biases. PLoS One
2021;16:e0247839.
42. Miroshnichenko AS, Mikhelev VM, editors. Classification of
Medical Images of Patients With COVID‑19 Using Transfer
Learning Technology of Convolutional Neural Network.
Volgograd, Russian: Journal of Physics: Conference Series; 2021.
43. Qi X, Brown LG, Foran DJ, Nosher J, Hacihaliloglu I. Chest X‑ray
image phase features for improved diagnosis of COVID‑19 using
convolutional neural network. Int J Comput Assist Radiol Surg
2021;16:197‑206.
44. Bhoumik S, Chatterjee S, Sarkar A, Kumar A, Joseph FJ, editors.
COVID 19 Prediction from X Ray Images Using Fully Connected
Convolutional Neural Network. Chengdu, China: ACM
International Conference Proceeding Series; 2020.
45. Dhamodharavadhani S, Rathipriya R, Chatterjee JM. COVID‑19
mortality rate prediction for India using statistical neural network
models. Front Public Health 2020;8:441.
46. Mónica JC, Melin P, Sánchez D. Genetic Optimization of Ensemble
Neural Network Architectures for Prediction of COVID‑19
Confirmed and Death Cases. Germany: Studies in Computational
Intelligence; 2021. p. 85‑98.
47. Rasjid ZE, Setiawan R, Effendi A. A comparison: Prediction of
death and infected COVID‑19 cases in Indonesia using time
series smoothing and LSTM neural network. Procedia Comput
Sci 2021;179:982‑8.
48. Tamang SK, Singh PD, Datta B. Forecasting of COVID‑19 cases
based on prediction using artificial neural network curve fitting
technique. Glob J Environ Sci Manage 2020;6:53‑64.
49. Agieb RS. Machine learning models for the prediction the
necessity of resorting to ICU of COVID‑19 patients. Int J Adv
Trends Comput Sci Eng 2020;9:6980‑4.
50. Dan T, Li Y, Zhu Z, Chen X, Quan W, Hu Y, et al., editors.
Machine Learning to Predict ICU Admission, ICU Mortality and
Survivors’ Length of Stay among COVID‑19 Patients: Toward
Optimal Allocation of ICU Resources. In: Proceedings – 2020
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