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.
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This work is funded by the Ministry of Science of Education and Sciences of Ukraine and Central Europenian Initiatives.
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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
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