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Piyanzin, A., Ashkinadze, A., Akinina, Z., & Romanova, O. (2018). Machine Learning Technologies and Psychological Testing of Pre-School and Primary School-Aged Children in Diagnostics of Perinatal Affection of the Central Nervous System. KnE Life Sciences, 4(8), 750–753. https://doi.org/10.18502/kls.v4i8.3332

https://doi.org/10.18502/kls.v4i8.3332

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authors

  • A Piyanzin
    No author data available.
  • A Ashkinadze
    No author data available.
  • Z Akinina
    No author data available.
  • O Romanova
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Published Date

  • Nov 1, 2018

Machine Learning Technologies and Psychological Testing of Pre-School and Primary School-Aged Children in Diagnostics of Perinatal Affection of the Central Nervous System

KnE Life Sciences / The Fifth International Luria Memorial Congress «Lurian Approach in International Psychological Science» / Pages 750–753

Abstract

The present study deals with computer-assisted learning technologies used for the analysis of psychological test results of children to diagnosis perinatal affection of the central nervous system. The mathematical models of logistic regression and gradient boosting give the best results within the accuracy of 81%.



Keywords: Machine learning, children, central nervous system, perinatal affection.

References
[1] Luria A.R. (1970). The functional organization of the brain. Scientific American, 3, 222


[2] Akhutina T.V., Korneev A.A., Matveeva E. Yu., Agris A.R. (2015). Age-related changes of higher mental functions in 7-9 years old children with different types of state regulation deficits. Journal of the Higher School of Economics, 12(3), 131-152.


[3] M Duda., N Haber., J Daniels., D P Wall. (2017). Crowdsourced validation of a machine-learning classification system for autism and ADHD. Transl Psychiatry, 7(5), e1133. doi: 10.1038/tp.2017.86


[4] Golden C.J. (1987). Luria-Nebraska Neuropsychological Battery: Children’s revision. Los Angeles, CA: Western Psychological Services. of socialization in modern adolescent. Psychology of learning. 4. 88-98.