Document Type : Original Article
Authors
1 Assistant Professor of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
2 Assistant Professor of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran, Assistant Professor of Health Information Management, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
3 Assistant Professor of Medical Informatics, School of Medicine, North Khorasan University of Medical Science, North Khorasan, Iran
4 Assistant Professor of Infectious Diseases, School of Medicine, Abadan University of Medical Sciences, Abadan, Iran
5 MSc of Health Information Technology, Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
Abstract
BACKGROUND: Given coronavirus disease (COVID‑19’s) unknown nature, diagnosis, and treatment
is very complex up to the present time. Thus, it is essential to have a framework for an early prediction
of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns
from mining of huge raw datasets then it establishes high‑quality predictive models. At this juncture,
we aimed to apply different ML techniques to develop clinical predictive models and select the best
performance of them.
MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID‑19 focal center
affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset
used in this study consists of 501 case records with two classes (COVID‑19 and non COVID‑19) and
32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random
forest (RF), multilayer perceptron, K‑star, C4.5, and support vector machine were developed. Then,
the recital of selected ML models was assessed by the comparison of some performance indices such
as accuracy, sensitivity, specificity, precision, F‑score, and receiver operating characteristic (ROC).
RESULTS: The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity
of 75.70%, precision of 92.30%, sensitivity of 92.40%, F‑measure of 92.00%, and ROC of 97.15%
has the best capability for COVID‑19 diagnosis and screening.
CONCLUSION: The empirical results reveal that RF model yielded higher performance as compared
to other six classification models. It is promising to the implementation of RF model in the health‑care
settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening,
surveillance, and early treatment.
Keywords
- Rao AS, Vazquez JA. Identification of COVID‑19 can be 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. Panahi S, Ashrafi‑Rizi H, Panahi M. Exposure to
coronavirus (COVID‑19) using narrative and simulated
experience approaches: A commentary. J Educ Health Promot
2020;9:135.5. Peeri NC, Shrestha N, Rahman MS, Zaki R, Tan Z, Bibi S, et al.
The SARS, MERS and novel coronavirus (COVID‑19) epidemics,
the newest and biggest global health threats: What lessons have
we learned? Int J Epidemiol 2020;49:717‑26.
6. Mackenzie JS, Smith DW. COVID‑19‑A novel zoonotic disease:
A review of the disease, the virus, and public health measures.
Asia Pac J Public Health 2020;32:145‑53.
7. Yoo HS, Yoo D. COVID‑19 and veterinarians for one health,
zoonotic‑and reverse‑zoonotic transmissions. J Vet Sci
2020;21:e51.
8. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R.
Features, evaluation and treatment coronavirus (COVID‑19). In:
StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing;
2020.
9. 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.
10. Shanbehzadeh M, Kazemi‑Arpanahi H, Mazhab‑Jafari K,
Haghiri H. Coronavirus disease 2019 (COVID‑19) surveillance
system: Development of COVID‑19 minimum data set and
interoperable reporting framework. J Educ Health Promot. 2020
Aug 31;9:203.
11. Jacobsen KH. Will COVID‑19 generate global preparedness?
Lancet 2020;395:1013‑4.
12. 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.
13. El Zowalaty ME, Järhult JD. From SARS to COVID‑19:
A previously unknown SARS‑related coronavirus (SARS‑CoV‑2)
of pandemic potential infecting humans – Call for a one health
approach. One Health 2020;9:100124.
14. 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.
15. 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.
16. ALOM, Md Zahangir, et al. COVID_MTNet: COVID‑19 Detection
with Multi‑Task Deep Learning Approaches. arXiv preprint arXiv:
2004.03747, 2020.
17. 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.
18. 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.
19. Hussain A, Bhowmik B, do Vale Moreira NC. COVID‑19 and
diabetes: Knowledge in progress. Diabetes Res Clin Pract
2020;162:108142.
20. 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.
21. Thabtah F, Peebles D. A new machine learning model based
on induction of rules for autism detection. Health Informatics J
2020;26:264‑86.
22. Gunčar G, Kukar M, Notar M, Brvar M, Černelč P, Notar M, et al.
An application of machine learning to haematological diagnosis.
Sci Rep 2018;8:1‑12.
23. Bryce C, Ring P, Ashby S, Wardman JK. Resilience in the face of
uncertainty: Early lessons from the COVID‑19 pandemic. JRisk Res
2020; 23 (7): 140‑8. [Doi: https://doi.org/10.1080/13669877.2020
0.1756379].
24. Chater N. Facing up to the uncertainties of COVID‑19. Nat Hum
Behav 2020;4:439.
25. Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, et al.
Artificial intelligence–enabled rapid diagnosis of patients with
COVID‑19. Nat Med. 2020 Aug; 26 (8):1224‑1228. [Doi: 10.1038/
s41591‑020‑0931‑3].
26. Wang Y, Song W, Zhao Z, Chen P, Liu J, Li C. The impacts of
viral inactivating methods on quantitative RT‑PCR for COVID‑19.
Virus Res 2020;285:197988.
27. Brogna B, Bignardi E, Salvatore P, Alberigo M, Brogna C,
Megliola A, et al. Unusual presentations of COVID‑19 pneumonia
on CT scans with spontaneous pneumomediastinum and
loculated pneumothorax: A report of two cases and a review of
the literature. Heart Lung 2020; 49:864‑68. [Doi: 10.1016/j. hrtlng.
2020.06.005].
28. Chen J, Peng S, Zhang B, Liu Z, Liu L, Zhang W. An uncommon
manifestation of COVID‑19 pneumonia on CT scan with small
cavities in the lungs: A case report. Medicine (Baltimore)
2020;99:e21240.
29. Gündüz Y, Öztürk MH, Tomak Y. The usual course of thorax
CT findings of COVID‑19 infection and when to perform control
thorax CT scan. Turk J Med Sci 2020;50:684‑6.
30. Hassanien AE, Salama A, Darwsih A. Artificial intelligence
approach to predict the COVID‑19 patient’s recovery. No 3223
Easy Chair; 2020.
31. Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, et al. Development
and evaluation of an artificial intelligence system for COVID‑19
diagnosis. Nat Commun 2020;11:5088.
32. Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, et al. Artificial
intelligence–enabled rapid diagnosis of patients with COVID‑19.
Nat Med 2020;26:1224‑8.
33. VaishyaR, JavaidM, KhanIH, HaleemA. Artificial intelligence (AI)
applications for COVID‑19 pandemic. Diabetes Metab Syndr
2020;14:337‑9.
34. Wong ZSY, Zhou J, Zhang Q. Artificial Intelligence for infectious
disease big data analytics. Infect Dis Health 2019;24:44‑8.
35. Wu CC, Yeh WC, Hsu WD, Islam MM, Nguyen PAA, Poly TN,
et al. Prediction of fatty liver disease using machine learning
algorithms. Comput Methods Programs Biomed 2019;170:23‑9.
36. Vinod DN, Prabaharan SRS. Data science and the role of artificial
intelligence in achieving the fast diagnosis of COVID‑19. Chaos
Solitons Fractals 2020;140:110182.
37. Albahri AS, Hamid RA, Alwan JK, Al‑Qays ZT, Zaidan AA,
Zaidan BB, et al. Role of biological data mining and machine
learning techniques in detecting and diagnosing the novel
coronavirus (COVID‑19): A systematic review. J Med Syst
2020;44:122.
38. Sharma S. Drawing insights from COVID‑19‑infected patients
using CT scan images and machine learning techniques: A study
on 200 patients. Environ Sci Pollut Res Int 2020;27:37155‑63.
39. Mantas J. Setting up an Easy‑to‑use machine learning pipeline for
medical decision support: A case study for COVID‑19 diagnosis
based on deep learning with CT scans. Importance Health Inform
Public Health Pandemic 2020;272:13.
40. Li WT, Ma J, Shende N, Castaneda G, Chakladar J, Tsai JC, et al.
Using machine learning of clinical data to diagnose COVID‑19:
A systematic review and meta‑analysis. BMC Med Inform Decis
Mak 2020;20:247.
41. Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC. Review
of big data analytics, artificial intelligence and nature‑inspired
computing models towards accurate detection of COVID‑19
pandemic cases and contact tracing. Int J Environ Res Public
Health 2020;17: 5330.
42. Shanbehzadeh M, Kazemi‑Arpanahi H. Development of minimal
basic data set to report COVID‑19. Med J Islam Repub Iran
2020;34:754‑63.
43. Chapman BP, Weiss A, Duberstein PR. Statistical learning theory
for high dimensional prediction: Application to criterion‑keyed
scale development. Psychol Methods 2016;21:603‑20. - 44. Chao CM, Yu YW, Cheng BW, Kuo YL. Construction the model
on the breast cancer survival analysis use support vector machine,
logistic regression and decision tree. J Med Syst 2014;38:106.
45. Wang GL, Li YF, Bi DX. Support vector networks in adaptive
friction compensation. IEEE Trans Neural Netw 2007;18:1209‑19.
46. Abdar M, Kalhori SR, Sutikno T, Subroto IM, Arji G. Comparing
performance of data mining algorithms in prediction heart
diseases. Int J Electr Comput Eng (2088‑8708) 2015;5:1569‑76. [Doi:
10.11591/ijece. v5i6.pp1569‑1576].
47. Kaur G, Chhabra A. Improved J48 classification algorithm for
the prediction of diabetes. Int J Comput Appl 2014;98:41. [Doi:
10.1186/s12911‑019‑0790‑3].
48. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, et al.
Top 10 algorithms in data mining. Knowl Inf Syst 2008;14:1‑37.
49. Ozçift A. Random forests ensemble classifier trained with data
resampling strategy to improve cardiac arrhythmia diagnosis.
Comput Biol Med 2011;41:265‑71.
50. Liaw A, Wiener M. Classification and regression by random forest.
R News. 2002;2:18‑22.
51. Amirkhani H, Rahmati M, Lucas PJF, Hommersom A. Exploiting
experts’ knowledge for structure learning of Bayesian networks.
IEEE Trans Pattern Anal Mach Intell 2017;39:2154‑70.
52. Zhang S, Tjortjis C, Zeng X, Qiao H, Buchan I, Keane J. Comparing
data mining methods with logistic regression in childhood obesity
prediction. Inf Syst Front 2009;11:449‑60.
53. Zhang Q, Deng D, Dai W, Li J, Jin X. Optimization of culture
conditions for differentiation of melon based on artificial neural
network and genetic algorithm. Sci Rep 2020;10:3524.
54. Cho YB, Farrokhkish M, Norrlinger B, Heaton R, Jaffray D,
Islam M. An artificial neural network to model response of a
radiotherapy beam monitoring system. Med Phys 2020;47:1983‑94.
55. Baitharu TR, Pani SK. Analysis of data mining techniques for
healthcare decision support system using liver disorder dataset.
Procedia Comput Sci 2016;85:862‑70.
56. Maliha SK, Islam T, Ghosh SK, Ahmed H, Mollick MR, Ema RR.
Prediction of cancer using logistic regression, K‑Star and J48
algorithm. 2019 4th International Conference on Electrical
Information and Communication Technology, EICT 2019; 2019.
57. Wiharto W, Kusnanto H, Herianto H. Intelligence system for
diagnosis level of coronary heart disease with K‑star algorithm.
Healthc Inform Res 2016;22:30‑8.
58. Han J, Pei J, Kamber M. Data mining: Concepts and techniques.
Amsterdam, Netherlands: Elsevier; 2011.
59. 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.
60. Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE.
Influenza forecasting in human populations: A scoping review.
PLoS One 2014;9:e94130.
61. Afshar S, Afshar S, Warden E, Manochehri H, Saidijam M.
Application of Artificial Neural Network in miRNA Biomarker
Selection and Precise Diagnosis of Colorectal Cancer Iran Biomed
J 2019;23:175‑83.
62. Lawson AB. Statistical methods in spatial epidemiology.
Hoboken, New Jersey: John Wiley & Sons; 2013.
63. 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.
64. Oliveira BA, Oliveira LC, Sabino EC, Okay TS. SARS‑CoV‑2 and
the COVID‑19 disease: A mini review on diagnostic methods.
Rev Inst Med Trop Sao Paulo 2020;62:e44.
65. Mahmood A, Gajula C, Gajula P. Clinical and diagnostic criteria of
COVID 19; a study of 4659 patients evaluating diagnostic testing
and establishing an algorithm. J Med Surg Sci 2020;2:2.
66. Rehm GB, Woo SH, Chen XL, Kuhn BT, Cortes‑Puch I,
Anderson NR, et al. Leveraging IoTs and Machine learning for
patient diagnosis and ventilation management in the intensive
care unit. IEEE Pervasive Comput 2020;19:68‑78.
67. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus
disease (COVID‑19) using X‑ray images and deep convolutional
neural networks. arXiv preprint arXiv2020;3:200310849.
68. Yasar H, Ceylan M. A novel comparative study for detection of
Covid‑19 on CT lung images using texture analysis, machine
learning, and deep learning methods. Multimed Tools Appl
2020;79:1‑25.
69. Moftakhar L, Mozhgan S, Safe MS. Exponentially increasing
trend of infected patients with COVID‑19 in Iran: A comparison
of neural network and ARIMA forecasting models. Iran J Public
Health 2020;49:92‑100.
70. Alakus TB, Turkoglu I. Comparison of deep learning approaches
to predict COVID‑19 infection. Chaos Solitons Fractals
2020;140:110120. [Doi: 10.1371/journal.pone. 0236621].
71. Sedik A, Iliyasu AM, El‑Rahiem A, Abdel Samea ME,
Abdel‑Raheem A, Hammad M, et al. Deploying machine and
deep learning models for efficient data‑augmented detection of
covid‑19 infections. Viruses 2020;12:769.
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