Volume 9, Issue 3 (2021)                   Health Educ Health Promot 2021, 9(3): 229-236 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Orooji A, Kazemi-Arpanahi H, Kaffashian M, Kalvandi G, Shanbehzadeh M. Comparing of Machine Learning Algorithms for Predicting ICU admission in COVID-19 hospitalized patients. Health Educ Health Promot 2021; 9 (3) :229-236
URL: http://hehp.modares.ac.ir/article-5-49481-en.html
1- Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS), Bojnurd, Iran
2- Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran
3- Department of Physiology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran
4- Department of Pediatrics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
5- Department of Health Information Technology, School of Paramedicine, Ilam University of Medical Sciences, Ilam, Iran , Mostafa.shanbezadeh@gmail.com
Abstract:   (981 Views)
Aims: The world hospital systems are presently facing many unprecedented challenges from COVID‐19 disease. Prediction the deteriorating or critical cases can help triage patients and assist in effective medical resource allocation. This study aimed to develop and validate a prediction model based on Machine Learning algorithms to predict hospitalized COVID-19 patients for transfer to ICU based on clinical parameters.
Materials & Methods: This retrospective, single-center study was conducted based on cumulative data of COVID-19 patients (N=1225) who were admitted from March 9, 2020, to December 20, 2020, to Mostafa Khomeini Hospital, affiliated to Ilam University of Medical Sciences (ILUMS), focal point center for COVID-19 care and treatment in Ilam, West of Iran. 13 ML techniques from six different groups applied to predict ICU admission. To evaluate the performances of models, the metrics derived from the confusion matrix were calculated. The algorithms were implemented using WEKA 3.8 software.
Findings: This retrospective study's median age was 50.9 years, and 664 (54.2%) were male. The experimental results indicate that Meta algorithms have the best performance in ICU admission risk prediction with an accuracy of 90.37%, a sensitivity of 90.35%, precision of 88.25%, F-measure of 88.35%, and ROC of 91%.
Conclusion: Machine Learning algorithms are helpful predictive tools for real-time and accurate ICU risk prediction in patients with COVID-19 at hospital admission. This model enables and potentially facilitates more responsive health systems that are beneficial to high-risk COVID-19 patients.
Full-Text [PDF 722 kb]   (241 Downloads) |   |   Full-Text (HTML)  (152 Views)  
Article Type: Original Research | Subject: Technology of Health Education
Received: 2021/01/24 | Accepted: 2021/05/15 | Published: 2021/07/4
* Corresponding Author Address: Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Research Blvd., Bangangab, Ilam, Iran. Postal code: 6939177143

1. 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(3):717-26. [Link] [DOI:10.1093/ije/dyaa033] [PMID] [PMCID]
2. Qiu H, Wu J, Hong L, Luo Y, Song Q, Chen D. Clinical and epidemiological features of 36 children with coronavirus disease 2019 (COVID-19) in Zhejiang, China: An observational cohort study. Lancet Infect Dis. 2020;20(6):689-96. [Link] [DOI:10.1016/S1473-3099(20)30198-5]
3. Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study. Lancet Infect Dis. 2020;20(4):425-34. [Link] [DOI:10.1016/S1473-3099(20)30086-4]
4. Zhao Z, Chen A, Hou W, Graham JM, Li H, Richman PS, et al. Prediction model and risk scores of ICU admission and mortality in COVID-19. Plos One. 2020;15(7):0236618. [Link] [DOI:10.1371/journal.pone.0236618] [PMID] [PMCID]
5. Hu H, Yao N, Qiu Y. Comparing rapid scoring systems in mortality prediction of critically ill patients with novel coronavirus disease. Acad Emerg Med. 2020;27(6):461-8. [Link] [DOI:10.1111/acem.13992] [PMID] [PMCID]
6. Gao Y, Cai G, Fang W, Li HY, Wang SY, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020;11(1):1-10. [Link] [DOI:10.1038/s41467-020-18684-2] [PMID] [PMCID]
7. Das AK, Mishra S, Gopalan SS. Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool. PeerJ. 2020;8:10083. [Link] [DOI:10.7717/peerj.10083] [PMID] [PMCID]
8. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R. Features, evaluation and treatment coronavirus (COVID-19). Statpearls. 2020; 3(8):8-17. [Link]
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. [Link] [DOI:10.1016/j.ijsu.2020.02.034] [PMID] [PMCID]
10. Yan L, Zhang HT, Goncalves J, Xiao Y, Wang M, Guo Y, et al. An interpretable mortality prediction model for COVID-19 patients. Nat Mach Intell. 2020;2(5):1-6. [Link] [DOI:10.1038/s42256-020-0180-7]
11. Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos Solitons Fractals. 2020;138:110137. [Link] [DOI:10.1016/j.chaos.2020.110137] [PMID] [PMCID]
12. Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Comparison of four data mining algorithms for predicting colorectal cancer risk. J Adv Med Biomed Res. 2021;29(133):100-8. [Persian] [Link] [DOI:10.30699/jambs.29.133.100]
13. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ. 2020;369:1328. [Link] [DOI:10.1136/bmj.m1328] [PMID] [PMCID]
14. Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: An international multicentre study. Eur Respir J. 2020;56(2):2001104. [Link] [DOI:10.1183/13993003.01104-2020] [PMID] [PMCID]
15. 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(8):1224-8. [Link] [DOI:10.1038/s41591-020-0931-3] [PMID] [PMCID]
16. Chin V, Samia NI, Marchant R, Rosen O, Ioannidis JP, Tanner MA, et al. A case study in model failure? COVID-19 daily deaths and ICU bed utilisation predictions in New York state. Eur J Epidemiol. 2020;35(8):733-42. [Link] [DOI:10.1007/s10654-020-00669-6] [PMID] [PMCID]
17. Booth AL, Abels E, McCaffrey P. Development of a prognostic model for mortality in COVID-19 infection using machine learning. Mod Pathol. 2021;34(3):522-31. [Link] [DOI:10.1038/s41379-020-00700-x] [PMID] [PMCID]
18. Yadaw AS, Li YC, Bose S, Iyengar PR, Bunyavanich S, Pandey G. Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. Lancet Digital Health. 2020;2(10):516-25. [Link] [DOI:10.1016/S2589-7500(20)30217-X]
19. Allenbach Y, Saadoun D, Maalouf G, Vieira M, Hellio A, Boddaert J, et al. Development of a multivariate prediction model of intensive care unit transfer or death: A French prospective cohort study of hospitalized COVID-19 patients. Plos One. 2020;15(10):0240711. [Link] [DOI:10.1371/journal.pone.0240711] [PMID] [PMCID]
20. Zhou Y, He Y, Yang H, Yu H, Wang T, Chen Z, et al. Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: A multi-center study in China. Scandinavian J Trauma Resusc Emerg Med. 2020;28(1):106. [Link] [DOI:10.1186/s13049-020-00795-w] [PMID] [PMCID]
21. Ryan L, Lam C, Mataraso S, Allen A, Green-Saxena A, Pellegrini E, et al. Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study. Ann Med Surg. 2020;59:207-16. [Link] [DOI:10.1016/j.amsu.2020.09.044] [PMID] [PMCID]
22. Agieb RS. Machine learning models for the prediction the necessity of resorting to icu of COVID-19 patients. Int J Adv Trends Computer Sci Eng. 2020;9(5):6980-4. [Link] [DOI:10.30534/ijatcse/2020/15952020]
23. Pan P, Li Y, Xiao Y, Han B, Su L, Su M, et al. Prognostic assessment of covid-19 in the intensive care unit by machine learning methods: Model development and validation. J Med Internet Res. 2020;22(11):23128. [Link] [DOI:10.2196/23128] [PMID] [PMCID]
24. Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, Gefen-Halevi S, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435-43. [Link] [DOI:10.1007/s11739-020-02475-0] [PMID] [PMCID]
25. Foieni F, Sala G, Mognarelli JG, Suigo G, Zampini D, Pistoia M, et al. Derivation and validation of the clinical prediction model for COVID-19. Intern Emerg Med. 2020;15(8):1409-14. [Link] [DOI:10.1007/s11739-020-02480-3] [PMID] [PMCID]
26. Zhang Y, Xin Y, Li Q, Ma J, Li S, Lv X, et al. Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications. Biomed Eng Online. 2017;16(1):125. [Link] [DOI:10.1186/s12938-017-0416-x] [PMID] [PMCID]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.