Abstract
The paper proposed the development of an approach to modeling the nature of the individual's illness based on the ensemble of machine learning algorithms. Some factors may adversely affect the conduct, interpretation and generalization of research results, and the understanding and interpretation of the phenomenon under study.
The paper proposed to improve the existing algorithm by introducing the stage of preliminary data clustering and presented. The probabilistic production dependencies mining algorithm ex-pressed using pseudocode.
The method for generating probabilistic production dependencies based on the as-sociative rules of sequential dependencies, which allows to determine hidden data dependencies not only at the level of tuples, but also at the subset of tuples.
The optimization of the known methods is that for each dependence through the hash table is determined by many dependencies with the same part of the result or the same conditional part. The combination does not occur with all other elementary dependencies, but only with the corresponding state merger.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Coelho, F.C., Cruz, O.G., Codeço, C.T.: Epigrass: a tool to study disease spread in complex networks. Source Code Biol. Med. 3(1), 1–9 (2008)
Krause, D.D.: State health mapper: An interactive, web-based tool for physician workforce planning, recruitment, and health services research. Southern Med. J. 108(11), 650–656 (2015)
Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Sibert, J.: AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software 27(2), 233–249 (2012)
Borshchev, A., Brailsford, S., Churilov, L., Dangerfield, B.: Multi-method modelling: AnyLogic. Discrete-event Simulation and System Dynamics for Management Decision Making, pp. 248–279 (2014)
Whitman, J., Jayaprakash, C.: Stochastic modeling of influenza spread dynamics with recurrences. PLoS ONE 15(4), e0231521 (2020)
Trawicki, M.B.: Deterministic Seirs Epidemic Model for Modeling Vital Dynamics, Vaccinations, and Temporary Immunity. Mathematics 5(1), 7 (2017)
Iwata, K., Miyakoshi, C.: A simulation on potential secondary spread of novel coronavirus in an exported country using a stochastic epidemic seir model. Journal of clinical medicine 9(4), 944 (2020)
Ivorra, B., Ferrández, M.R., Vela-Pérez, M., Ramos, A.M.: Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China. In: Communications in Nonlinear Science and Numerical Simulation, p. 105303 (2020)
Butt, C., Gill, J., Chun, D., Babu, B.A.: Deep learning system to screen coronavirus disease 2019 pneumonia. Appl. Intell. 1 (2020)
Tkachenko, R., Izonin, I.: Model and principles for the implementation of neural-like structures based on geometric data transformations. In: Hu, Z.B., Petoukhov, S. (eds.) Advances in Computer Science for Engineering and Education. ICCSEEA2018. Advances in Intelligent Systems and Computing, vol. 754, pp. 578–587. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91008-6_58
Agapito, G., Guzzi, P.H., Cannataro, M.: Parallel extraction of association rules from genomics data. Appl. Math. Comput. 350, 434–446 (2019)
Havens, T.C., Bezdek, J.C.: An efficient formulation of the improved visual assessment of cluster tendency (iVAT) algorithm. IEEE Trans. Knowl. Data Eng. 24(5), 813–822 (2011)
Dinh, D. T., Fujinami, T., Huynh, V. N.: Estimating the optimal number of clusters in categorical data clustering by silhouette coefficient. In: International Symposium on Knowledge and Systems Sciences, p. 17. Springer, Singapore, November 2019
Melnykova, N., Melnykov, V., Vasilevskis, E.: The personalized approach to the processing and analysis of patients' medical data. In: IDDM, pp. 103–112 (2018)
Shakhovska, N., Kaminskyy, R., Zasoba, E., Tsiutsiura, M.: Association rules mining in big data. Int. J. Comput. 17(1), 25–32 (2018)
Acknowledgments
This work is funded by the Ministry of Science of Education and Sciences of Ukraine and Central Europenian Initiatives.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shakhovska, N., Melnykova, N., Melnykov, V., Mahlovanyj, V., Hrabovska, N. (2021). The Novel Approach to Modeling the Spread of Viral Infections. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-63270-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63269-4
Online ISBN: 978-3-030-63270-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)