KnE Life Sciences

ISSN: 2413-0877

The latest conference proceedings on life sciences, medicine and pharmacology.

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

Published date: Nov 01 2018

Journal Title: KnE Life Sciences

Issue title: The Fifth International Luria Memorial Congress «Lurian Approach in International Psychological Science»

Pages: 750–753

DOI: 10.18502/kls.v4i8.3332

Authors:
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.

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