Developing an Intelligent System for Diagnosis of COVID-19 Based on Artificial Neural Network
Abstract
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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30. Li H, Wang Y, Zhen ZZ, Tan Y, Chen Z, Wang X, et al. Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model. IEEE/ACM Trans Comput Biol Bioinform 2021;18:2502-13.
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34. Lawson AB. Statistical methods in spatial epidemiology USA: John Wiley & Sons; 2013.
35. Yang WJ, Wu L, Mei ZM, Xiang Y. The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy. J Ophthalmol 2020;2020:1024926.
36. Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals 2020;139:110059.
37. Lopez-Betancur D, Duran R, Guerrero-Mendez C, Rodriguez R, Anaya T. Comparison of Convolutional Neural Network Architectures for COVID-19 Diagnosis. Comp y Sist 2021;25:601-15.
38. Kirisci M, Demir I, Simsek N. A Neural Network-Based Comparative Analysis for the Diagnosis of Emerging Different Diseases Based on COVID-19. Conference proceedings of science and technology; 2021.
39. Goel T, Murugan R, Mirjalili S, Chakrabartty DK. OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Appl Intell (Dordr) 2021;51:1351-66.
40. Lessage X, Mahmoudi S, Mahmoudi SA, Laraba S, Debauche O, Belarbi MA. Chest X-ray Images Analysis with Deep Convolutional Neural Networks (CNN) for COVID-19 Detection. Healthcare Informatics for Fighting COVID-19 and Future Epidemics. Switzerland: Springer; 2022:403-23.
41. Biradar VG, Sanjay H, Nagaraj H. Convolutional Neural Networks for COVID-19 Diagnosis. Understanding COVID-19: The Role of Computational Intelligence. Switzerland: Springer; 2022:501-29.
2. Mirsoleymani S, Taherifard E, Taherifard E, Taghrir MH, Marzaleh MA, Peyravi M, et al. Predictors of Mortality Among COVID-19 Patients With or Without Comorbid Diabetes Mellitus. Acta Med Iran 2021;59:393-9.
3. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R. Features, evaluation and treatment coronavirus (COVID-19). StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022.
4. 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.
5. Jacobsen KH. Will COVID-19 generate global preparedness? Lancet 2020;395:1013-4.
6. 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.
7. 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.
8. Torrealba-Rodriguez O, Conde-Gutiérrez R, Hernández-Javier A. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos Solitons Fractals 2020;138:109946.
9. 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. J Med Internet Res 2020;22:e19786.
10. Alom MZ, Rahman M, Nasrin MS, Taha TM, Asari VK. COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches. arXiv 2020.
11. 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.
12. 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.
13. Hussain A, Bhowmik B, do Vale Moreira NC. COVID-19 and diabetes: Knowledge in progress. Diabetes Res Clin Pract 2020;162:108142.
14. 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.
15. 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.
16. Wu SY, Yau HS, Yu MY, Tsang HF, Chan LWC, 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.
17. Hassanien AE, Salama A, Darwsih A. Artificial Intelligence Approach to Predict the COVID-19 Patient's Recovery. EasyChair 2020.
18. 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. Nat Commun 2020;11:5088.
19. Sipior JC. Considerations for Development and Use of AI in Response to COVID-19. Int J Inf Manage 2020;55:102170.
20. Wong ZS, Zhou J, Zhang Q. Artificial intelligence for infectious disease big data analytics. Infect Dis Health 2019;24:44-8.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. Yazdani A, Safdari R, Ghazisaeedi M, Beigy H, Sharifian R. Scalable Architecture for Telemonitoring Chronic Diseases in Order to Support the CDSSs in a Common Platform. Acta Inform Med 2018;26:195-200.
27. 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.
28. Hasani M, Emami F. Evaluation of feed-forward back propagation and radial basis function neural networks in simultaneous kinetic spectrophotometric determination of nitroaniline isomers. Talanta 2008;75:116-26.
29. Sapna S, Tamilarasi A, Kumar MP. Backpropagation learning algorithm based on Levenberg Marquardt Algorithm. Comp Sci Inform Technol 2012;2:393-8.
30. Li H, Wang Y, Zhen ZZ, Tan Y, Chen Z, Wang X, et al. Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model. IEEE/ACM Trans Comput Biol Bioinform 2021;18:2502-13.
31. Li X, Cheng X, Wu W, Wang Q, Tong Z, Zhang X, et al. Forecasting of bioaerosol concentration by a Back Propagation neural network model. Sci Total Environ 2020;698:134315.
32. Adnan Ja, Daud NGN, Ishak MT, Rizman ZI, Rahman MIA. Tansig activation function (of MLP network) for cardiac abnormality detection. AIP Conference Proceedings. Maryland: AIP Publishing LLC, 2018.
33. Saba AI, Elsheikh AH. Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process Saf Environ Prot 2020;141:1-8.
34. Lawson AB. Statistical methods in spatial epidemiology USA: John Wiley & Sons; 2013.
35. Yang WJ, Wu L, Mei ZM, Xiang Y. The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy. J Ophthalmol 2020;2020:1024926.
36. Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals 2020;139:110059.
37. Lopez-Betancur D, Duran R, Guerrero-Mendez C, Rodriguez R, Anaya T. Comparison of Convolutional Neural Network Architectures for COVID-19 Diagnosis. Comp y Sist 2021;25:601-15.
38. Kirisci M, Demir I, Simsek N. A Neural Network-Based Comparative Analysis for the Diagnosis of Emerging Different Diseases Based on COVID-19. Conference proceedings of science and technology; 2021.
39. Goel T, Murugan R, Mirjalili S, Chakrabartty DK. OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Appl Intell (Dordr) 2021;51:1351-66.
40. Lessage X, Mahmoudi S, Mahmoudi SA, Laraba S, Debauche O, Belarbi MA. Chest X-ray Images Analysis with Deep Convolutional Neural Networks (CNN) for COVID-19 Detection. Healthcare Informatics for Fighting COVID-19 and Future Epidemics. Switzerland: Springer; 2022:403-23.
41. Biradar VG, Sanjay H, Nagaraj H. Convolutional Neural Networks for COVID-19 Diagnosis. Understanding COVID-19: The Role of Computational Intelligence. Switzerland: Springer; 2022:501-29.
| Files | ||
| Issue | Vol 60, No 3 (2022) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/acta.v60i3.9000 | |
| Keywords | ||
| Coronavirus disease 2019 (COVID-19) Artificial neural network Intelligent system | ||
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How to Cite
1.
Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Developing an Intelligent System for Diagnosis of COVID-19 Based on Artificial Neural Network. Acta Med Iran. 2022;60(3):135-143.
