Machine learning techniques combination for selective and hierarchical analysis of psycho diagnostic data

Elena V. Slavutskaya; Stella G. Grigoryeva; Larisa G. Grigoryeva; Seimbika U. Bichurina

doi:10.1117/12.2680701

27 April 2023 Machine learning techniques combination for selective and hierarchical analysis of psycho diagnostic data

Elena V. Slavutskaya, Stella G. Grigoryeva, Larisa G. Grigoryeva, Seimbika U. Bichurina

Proceedings Volume 12637, International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023); 126370B (2023) https://doi.org/10.1117/12.2680701
Event: International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023), 2023, Fergana, Uzbekistan

Abstract

The research is devoted to the use of the decision tree method and artificial neural networks for the analysis of psycho diagnostic data. The possibility of applying these two data mining tools together to obtain additional information is explored. The analysis is carried out on the example of the psychological testing results of preschoolers’ parents. Two widely known tests were used, which allow to assess the relationship between 27 psychological indicators having different numerical scales. The sample consisted of 496 respondents. It is shown that the combined use of the indicated processing techniques allows for the selective and hierarchical assessment of the interrelationships of multidimensional heterogeneous data. The proposed approach is available to a wide range of specialists from different fields of knowledge, since the tools used are included in widespread software.

Citation Download Citation

Elena V. Slavutskaya, Stella G. Grigoryeva, Larisa G. Grigoryeva, and Seimbika U. Bichurina "Machine learning techniques combination for selective and hierarchical analysis of psycho diagnostic data", Proc. SPIE 12637, International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023), 126370B (27 April 2023); https://doi.org/10.1117/12.2680701

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