References
1. Alizadehsani R, Behjati M, Roshanzamir Z, Hussain S, Abedini N,
Hasanzadeh F, et al. Risk Factors Prediction, Clinical Outcomes, and
Mortality of COVID-19 Patients. medRxiv. 2020.
2. Zheng Y-Y, Ma Y-T, Zhang J-Y, Xie X. COVID-19 and the cardiovascular
system. Nature Reviews Cardiology. 2020;17(5):259-60.
3. Sugiyama M, Kinoshita N, Ide S, Nomoto H, Nakamoto T, Saito S, et al.
Serum CCL17 level becomes a predictive marker to distinguish between
mild/moderate and severe/critical disease in patients with COVID-19.
Gene. 2020;766:145145.
4. Khan W, Hussain A, Khan SA, Al-Jumailey M, Nawaz RJapa. Association
Learning Between the COVID-19 Infections and Global Demographic
Characteristics Using the Class Rule Mining and Pattern Matching. 2020.
5. Alizadehsani R, Behjati M, Roshanzamir Z, Hussain S, Abedini N,
Hasanzadeh F, et al. Risk Factors Prediction, Clinical Outcomes, and
Mortality of COVID-19 Patients. 2020.
6. Salepci E, Turk B, Ozcan SN, Bektas ME, Aybal A, Dokmetas I, et al.
Symptomatology of COVID-19 from the otorhinolaryngology perspective: a
survey of 223 SARS-CoV-2 RNA-positive patients. 2020:1-11.
7. Salepci E, Turk B, Ozcan SN, Bektas ME, Aybal A, Dokmetas I, et al.
Otorhinolaryngologic and General Symptoms Survey of 223 COVID-19
Patients. V1 ed. Mendeley Data2020.
8. Feng W, Huang W, Ren JJAS. Class imbalance ensemble learning based on
the margin theory. 2018;8(5):815.
9. Hofmann M, Klinkenberg R. RapidMiner: Data mining use cases and
business analytics applications: CRC Press; 2016.
10. Kotu V, Deshpande B. Predictive analytics and data mining: concepts
and practice with rapidminer: Morgan Kaufmann; 2014.
11. Modeler IS. Algorithms Guide.[(accessed on 25 November 2020)].
12. Andrei NJCO, Applications. Scaled conjugate gradient algorithms for
unconstrained optimization. 2007;38(3):401-16.
13. Feng L, Li Z, Wang Y, Zheng C, Guan Y, editors. VLSI design of
modified sequential minimal optimization algorithm for fast SVM
training. 2016 13th IEEE International Conference on Solid-State and
Integrated Circuit Technology (ICSICT); 2016: IEEE.
14. Tang J, Ning J, Liu X, Wu B, Hu RJCC-ADD. A novel amino acid
sequence-based computational approach to predicting cell-penetrating
peptides. 2019;15(3):206-11.
15. McCormick K, Salcedo J. IBM SPSS Modeler essentials: Effective
techniques for building powerful data mining and predictive analytics
solutions: Packt Publishing Ltd; 2017.
16. Wendler T, Gröttrup S. Data mining with SPSS modeler: theory,
exercises and solutions: Springer; 2016.
17. Mierswa I, Klinkenberg R. RapidMiner Studio (9.8)[Data science,
machine learning, predictive analytics]. 2020.
18. S. Y, Arslan AK, Yologlu S, Colak C. DTROC: Tanı Testleri ve ROC
Analizi Yazılımı 2019 [Available from:http://biostatapps.inonu.edu.tr/DTROC/.
19. Chicco D, Jurman GJBg. The advantages of the Matthews correlation
coefficient (MCC) over F1 score and accuracy in binary classification
evaluation. 2020;21(1):6.
20. Released IC. IBM SPSS Statistics for Windows, Version 26.0. IBM Corp
Armonk, NY; 2019.
21. IBM_Corp R. IBM SPSS Modeler for Windows, Version 18.0. IBM Corp,
Armonk, NY. 2016.
22. Pustokhin DA, Pustokhina IV, Dinh PN, Phan SV, Nguyen GN, Joshi GP.
An effective deep residual network based class attention layer with
bidirectional LSTM for diagnosis and classification of COVID-19. Journal
of Applied Statistics. 2020:1-18.
23. Yip SS, Klanecek Z, Naganawa S, Kim J, Studen A, Rivetti L, et al.
Performance and Robustness of Machine Learning-based Radiomic COVID-19
Severity Prediction. 2020.
24. Feng S, Keung J, Yu X, Xiao Y, Bennin KE, Kabir MA, et al. COSTE:
Complexity-based OverSampling TEchnique to alleviate the class imbalance
problem in software defect prediction. 2020;129:106432.
25. Dong X, Yu Z, Cao W, Shi Y, Ma QJFoCS. A survey on ensemble
learning. 2020:1-18.
26. Zhou K, Sun Y, Li L, Zang Z, Wang J, Li J, et al. Eleven Routine
Clinical Features Predict COVID-19 Severity Uncovered by Machine
Learning of Longitudinal Measurements. 2020.
27. Overmyer KA, Shishkova E, Miller IJ, Balnis J, Bernstein MN,
Peters-Clarke TM, et al. Large-scale multi-omic analysis of COVID-19
severity. Cell systems. 2020.
28. de Terwangne C, Laouni J, Jouffe L, Lechien JR, Bouillon V, Place S,
et al. Predictive Accuracy of COVID-19 World Health Organization (WHO)
Severity Classification and Comparison with a Bayesian-Method-Based
Severity Score (EPI-SCORE). Pathogens. 2020;9(11):880.
29. Ali AM, Ghafoor KZ, Maghdid HS, Mulahuwaish A. Diagnosing COVID-19
Lung Inflammation Using Machine Learning Algorithms: A Comparative
Study. Internet of Medical Things for Smart Healthcare: Springer; 2020.
p. 91-105.
Table 1: The detailed explanation of the variables/attributes
in the dataset