Decision-tree and ensemble-based mortality risk models for hospitalized patients with COVID-19




COVID-19, decision-making system, decision tree, ML-ensemble, ensemble of classification models


The work is devoted to studying SARS-CoV-2-associated pneumonia and the investigating of the main indicators that lead to the patients’ mortality. Using the good-known parameters that are routinely embraced in clinical practice, we obtained new functional dependencies based on an accessible and understandable decision tree and ML ensemble of classifiers models that would allow the physician to determine the prognosis in a few minutes and, accordingly, to understand the need for treatment adjustment, transfer of the patient to the emergency department. The accuracy of the resulting ensemble of models fitted on actual hospital patient data was in the range of 0.88–0.91 for different metrics. Creating a data collection system with further training of classifiers will dynamically increase the forecast’s accuracy and automate the doctor’s decision-making process.

Author Biographies

Yaroslav Vyklyuk, Lviv Polytechnic National University, Lviv

Doctor of Technical Sciences, a professor at the Department of Artificial Intelligence Systems of Lviv Polytechnic National University, Lviv, Ukraine.

Svitlana Levytska, Bukovinian State Medical University, Chernivtsi

Doctor of Medical Sciences, a professor at the Department of Pediatric Surgery and Otorhinolaryngology of Bukovinian State Medical University, Chernivtsi, Ukraine.

Denys Nevinskyi, Lviv Polytechnic National University, Lviv

Associate professor, Candidate of Technical Sciences (Ph.D.), an assistant of the Department of Electronics and Information Technology of the Institute of Telecommunications, Radioelectronics and Electronic Engineering of Lviv Polytechnic National University, Lviv, Ukraine.

Kateryna Hazdiuk, Yuriy Fedkovych Chernivtsi National University, Chernivtsi

Ph.D., an assistant professor at the Department of Computer Systems Software of Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine.

Miroslav Škoda, DTI University, Dubnica nad Vahom

Ph.D., Vice-Rector for International Relations and Accreditation of DTI University, Dubnica nad Vahom, Slovakia.

Stanislav Andrushko, Chernivtsi Central Hospital, Chernivtsi

Otolaryngologist at Chernivtsi Central Hospital, Chernivtsi, Ukraine.

Maryna Palii, Chernivtsi Central Hospital, Chernivtsi

Otolaryngologist at Chernivtsi Central Hospital, Chernivtsi, Ukraine.


Worldometer COVID-19 Coronavirus Pandemic. 2020. Accessed on: November 28, 2021. [Online]. Available:

S. Priya, M. Selva Meena, J. Sangumani, P. Rathinam, C. Brinda Priyadharshini, and V. Vijay Anand, “Factors influencing the outcome of COVID-19 patients admitted in a tertiary care hospital, Madurai. -a cross-sectional study,” Clin Epidemiol Glob Health, 2021. doi: 10.1016/j.cegh.2021.100705.

Annemarie Jutel, “Classification, Disease, and Diagnosis,” Perspectives in Biology and Medicine, Project MUSE, vol. 54 no. 2, pp. 189–205, 2011. doi: 10.1353/pbm.2011.0015.

Aiping Lu, Miao Jiang, Chi Zhang, and Kelvin Chan, “An integrative approach of linking tradi-tional Chinese medicine pattern classification and biomedicine diagnosis,” Journal of Ethnopharmacology, vol. 141, issue 2, pp. 549–556, 2012. Available:

O.S. Albahri et al., “Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects,” Journal of Infection and Public Health, vol. 13, issue 10, pp. 1381–1396, 2020. Available:

Gonçalo Marques, Deevyankar Agarwal, and Isabel de la Torre Díez, “Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network,” Applied Soft Computing, 2020, vol. 96. Available:

X. Wang et al., “A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2615–2625, 2020. doi: 10.1109/TMI.2020.2995965.

M.E.H. Chowdhury et al., “An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning,” Cogn. Comput., 2021. Available:

Li Tan et al., “Validation of Predictors of Disease Severity and Outcomes in COVID-19 Patients: A Descriptive and Retrospective Study,” Med, vol. 1, issue 1, pp. 128–138, 2020. Available:

Ashutosh Kumar Dubey, Sushil Narang, Abhishek Kumar, Sasubilli Satya Murthy, and Vicente García-Díaz, “Performance Estimation of Machine Learning Algorithms in the Factor Analysis of COVID-19 Dataset,” Computers, Materials, & Continua, 66(2), pp. 1921–1936, 2021.

Danying Liao et al., “Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: a retrospective cohort study,” The Lancet Haematology, vol. 7, issue 9, pp. e671–e678, 2020. Available:

M. Aguiar and N. Stollenwerk, “Condition-specific mortality risk can explain differences in COVID-19 case fatality ratios around the globe,” Public Health, vol. 188, pp. 18–20, 2020.

Rocio Laguna-Goya et al., “IL-6–based mortality risk model for hospitalized patients with COVID-19,” Journal of Allergy and Clinical Immunology, vol. 146, issue 4, pp. 799–807, 2020. Available:

R. Rana and R. Singhal, “Chi-square test and its application in hypothesis testing,” J. Pract. Cardiovasc. Sci., 1, pp. 69–71, 2015. doi: 10.4103/2395-5414.157577.

B.C. Ross, “Mutual Information between Discrete and Continuous Data Sets,” PLoS ONE, 9(2), 2014. Available:

E. Archer, I.M. Park, and J. Pillow, “Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data,” Entropy, 15 (12), pp. 1738–1755, 2013. doi: 10.3390/e15051738.

S.R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology”, Systems Man and Cybernetics IEEE Transactions, vol. 21, no. 3, pp. 660–674, 1991.

Laura Elena Raileanu and Kilian Stoffel, “Theoretical Comparison between the Gini Index and Information Gain Criteria,” Annals of Mathematics and Artificial Intelligence, vol. 41, pp. 77–93, 2004. doi: 10.1023/B:AMAI.0000018580.96245.c6.

J. Wagner, A. DuPont, S. Larson, B. Cash, and A. Farooq, “Absolute lymphocyte count is a prognostic marker in Covid-19: A retrospective cohort review,” Int. J. Lab. Hematol., vol. 42(6), pp. 761–765, 2020. doi: 10.1111/ijlh.13288.

A. Mazzoni, L. Salvati, L. Maggi, F. Annunziato, and L. Cosmi, “Hallmarks of immune response in COVID-19: Exploring dysregulation and exhaustion,” Semin. Immunol., 2021. doi: 10.1016/j.smim.2021.101508.

J. Wang, M. Jiang, X. Chen, and L.J. Montaner, “Cytokine storm and leukocyte changes in mild versus severe SARS-CoV-2 infection: Review of 3939 COVID-19 patients in China and emerging pathogenesis and therapy concepts,” J. Leukoc. Biol., 108(1), pp. 17–41, 2020. doi: 10.1002/JLB.3COVR0520-272R.

D. Böning, W.M. Kuebler, and W. Bloch, “The oxygen dissociation curve of blood in COVID-19,” Am. J. Physiol. Lung. Cell. Mol. Physiol., vol. 321(2), L349–L357, 2021. doi: 10.1152/ajplung.00079.2021.

J. Tolles and W.J. Meurer, “Logistic Regression: Relating Patient Characteristics to Outcomes,” JAMA, vol. 316(5), pp. 533–534, 2016. doi: 10.1001/jama.2016.7653.

Alaa Tharwat, “Linear vs. quadratic discriminant analysis classifier: a tutorial,” International Journal of Applied Pattern Recognition, vol. 3.2, pp. 145–180, 2016.

P. Domingos and M. Pazzani, “On the optimality of the simple Bayes-ian classifier under zero-one loss,” Machine Learning, vol. 29, pp. 103–137, 1997.

Leo Breiman, “Random Forests,” Machine Learning, 45 (1), pp. 5–32, 2001. doi: 10.1023/A:1010933404324.

Zhao Yan, Xing Chen, and Jun Yin, “Adaptive boosting-based computational model for predicting potential miRNA-disease associations,” Bioinformatics, vol. 35.22, pp. 4730–4738, 2019.

Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, A Practical Guide to Support Vector Classification. Department of Computer Science, National Taiwan University (Hrsg.), 2003.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” Proceedings of COMPSTAT, Physica-Verlag HD, 2010, pp. 177–186.

Shaohua Wan et al., “Deep multi-layer perceptron classifier for behavior analysis to estimate parkinson’s disease severity using smartphones,” IEEE Access, 6, pp. 36825–36833, 2018.

D.C. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming, vol. 45, pp. 503–528, 1989. Available:

Ph. Moritz, N. Robert, and M. Jordan, “A linearly-convergent stochastic L-BFGS algorithm,” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR, 51, pp. 249–258, 2016.

S. Madeh Piryonesi and Tamer El-Diraby, “Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index,” Journal of Infrastructure Systems, 26 (1): 04019036, 2020. doi: 10.1061/(ASCE)IS.1943-555X.0000512.

T. Hastie, R. Tibshirani, and J.H. Friedman, “Boosting and Additive Trees,” The Elements of Statistical Learning (2nd ed.). New York: Springer, 2009, pp. 337–384.

Onan Aytuğ, Serdar Korukoğlu, and Hasan Bulut, “A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification,” Expert Systems with Applications, vol. 62, pp. 1–16, 2016.






Decision making and control in economic, technical, ecological and social systems