A Machine Learning Model to Predict COVID-19 Infection Risk in the United Arab Emirates
Abstract
Background: One of the biggest challenges that public health officials face is to identify the infection risk among the population and the same scenario was repeated during the COVID-19 period. It was crucial to
identify the patients at high risk who needed immediate care, and accordingly, an optimal machine learning model has been developed for the prediction of infection risk which can be modified and reused.
Objective: The main objective of this article is to predict the COVID-19 infection risk of a patient. The predictive model would help in developing comprehensive plans to respond to both clinical treatments and control
of the spread.
Method: A retrospective study was carried out on 23,996 encounters in a nationwide cohort in the United Arab Emirates. Patient information was collected retrospectively from respective physicians and was uploaded in a patient under investigation (PUI) form filled during the COVID-19 screening. The variables were age, gender, body temperature, comorbidities and patient symptoms. This data (age, gender, body temperature, comorbidities and patient symptoms) was fed into AIbased machine learning algorithms to come up with a COVID-19 infection prediction model.
Results: The study found that 3818 (15.9%) cases were COVID-19 positive. Based on model performance, parsimony, and explainability, the Gradient Boosting Model was finally selected. The area under the
curve (AUC) for this model was 0.746 on the training dataset and 0.736 on the validation dataset.
Conclusion: A highly interpretable machine learning model comprising multiple patient characteristics and symptoms was developed to predict COVID-19 infection in patients.
How to cite this article:
AlShaya S, Jaffar MS, Mohideen SAJ, Gopakumar A, Mathur V, Saeed M. A Machine Learning Model to Predict COVID-19 Infection Risk in the United Arab Emirates. J Commun Dis. 2023;55(4):71-79.
DOI: https://doi.org/10.24321/0019.5138.202358
References
Du Y, Tu L, Zhu P, Mu M, Wang R, Yang P, Wang X, Hu C, Ping R, Hu P, Li T, Cao F, Chang C, Hu Q, Jin Y, Xu G. Clinical features of 85 fatal cases of COVID-19 from Wuhan. A retrospective observational study. Am J Respir Crit Care Med. 2020 Jun 1;201(11):1372-9. [PubMed] [Google Scholar]
Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, Li Q, Jiang C, Zhou Y, Liu S, Ye C, Zhang P, Xing Y, Guo H, Tang
W. Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis. J Infect.
Aug;81(2):e16-25. [PubMed] [Google Scholar]
Izcovich A, Ragusa MA, Tortosa F, Marzio MA, Agnoletti C, Bengolea A, Ceirano A, Espinosa F, Saavedra E, Sanguine V, Tassara A, Cid C, Catalano HN, Agarwal A, Foroutan F, Rada G. Prognostic factors for severity and mortality in patients infected with COVID-19: a systematic review. PLoS One. 2020 Nov 17;15(11):e0241955. [PubMed] [Google Scholar]
Argenziano MG, Bruce SL, Slater CL, Tiao JR, Baldwin MR, Barr RG, Chang BP, Chau KH, Choi JJ, Gavin N, Goyal P,
Mills AM, Patel AA, Romney ML, Safford MM, Schluger NW, Sengupta S, Sobieszczyk ME, Zucker JE, Asadourian
PA, Bell FM, Boyd R, Cohen MF, Colquhoun MI, Colville LA, de Jonge JH, Dershowitz LB, Dey SA, Eiseman KA,
Girvin ZP, Goni DT, Harb AA, Herzik N, Householder S, Karaaslan LE, Lee H, Lieberman E, Ling A, Lu R, Shou
AY, Sisti AC, Snow ZE, Sperring CP, Xiong Y, Zhou HW, Natarajan K, Hripcsak G, Chen R. Characterization
and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series.
BMJ. 2020;369:m1996. [PubMed] [Google Scholar]
Daoust JF. Elderly people and responses to COVID-19 in 27 countries. PLoS One. 2020;15(7):e0235590. [PubMed] [Google Scholar]
Li J, Chen Z, Nie Y, Ma Y, Guo Q, Dai X. Identification of symptoms prognostic of COVID-19 severity: multivariate
data analysis of a case series in Henan province. J Med Internet Res. 2020 Jun 30;22(6):e19636. [PubMed] [Google Scholar]
Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, Cao J, Tan M, Xu W, Zheng F, Shi Y, Hu B. A tool for early prediction
of severe Coronavirus Disease 2019 (COVID-19): a multicenter study using the Risk Nomogram in Wuhan
and Guangdong, China. Clin Infect Dis. 2020;71(15):833- 40. [PubMed] [Google Scholar]
Li D, Zhang Q, Tan Y, Feng X, Yue Y, Bai Y, Li J, Li J, Xu Y, Chen S, Xiao SY, Sun M, Li X, Zhu F. Prediction of
COVID-19 severity using chest computed tomography and laboratory measurements: evaluation using a
machine learning approach. JMIR Med Inform. 2020;8(11):e21604. [PubMed] [Google Scholar]
Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T, Wu Z, He G, Huang Y. Towards
an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput
Mater Contin. 2020;63(1):537-51. [Google Scholar]
Ma X, Li A, Jiao M, Shi Q, An X, Feng Y, Xing L, Liang H, Chen J, Li H, Li J, Ren Z, Sun R, Cui G, Zhou Y, Cheng
M, Jiao P, Wang Y, Xing J, Shen S, Zhang Q, Xu A, Yu
Z. Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model. Front Public Health.
;8:475. [PubMed] [Google Scholar]
Quiroz-Juárez MA, Torres-Gómez A, Hoyo-Ulloa I, León-Montiel RJ, U’Ren AB. Identification of high-risk
COVID-19 patients using machine learning. PLoS One. 2021;16(9):e0257234. [PubMed] [Google Scholar]
Majumder AB, Gupta S, Singh D, Majumder S. An intelligent system for prediction of COVID-19 case
using machine learning framework-logistic regression. J Phys Conf Ser. 2021;1797:012011. [Google Scholar]
Ghafouri-Fard S, Mohammad-Rahimi H, Motie P, Minabi MA, Taheri M, Nateghinia S. Application of machine
learning in the prediction of COVID-19 daily new cases: a scoping review. Heliyon. 2021;7(10):e08143.
[PubMed] [Google Scholar]
Devaraj J, Elavarasan RM, Pugazhendhi R, Shafiullah GM, Ganesan S, Jeysree AK, Khan IA, Hossain E. Forecasting of COVID-19 cases using deep learning models: is it reliable and practically significant? Results
Phys. 2021;21:103817. [PubMed] [Google Scholar]
Zoabi Y, Deri-Rozov S, Shomron N. Machine learningbased prediction of COVID-19 diagnosis based on symptoms. NPJ Digit Med. 2021;4(1):3. [PubMed] [Google Scholar].
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