Predicting mental disorders in minors using neural network data analysis
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Bodagova EA, Govorin NV, Mudrov VA. [Predicting mental disorders in minors using neural network data analysis]. Rossiiskii psikhiatricheskii zhurnal [Russian Journal of Psychiatry]. 2023;(5):45-54. Russian
In order to assess the possibility of predicting mental disorders in minors using neural network data analysis, an original study consisting of several stages was conducted. At the first stage, a clinical examination was conducted of 516 children left without parental care, and 100 children and adolescents living in their biological full families.
At the second stage, molecular genetic typing was performed, the frequencies of genotypes and alleles of polymorphic gene variants were determined: rs1800532 TPH1(A218C), rs6323 MAOA (R297R), rs6313 HTR2A (T102C), rs1800955 DRD4 (C-521T), rs4680 COMT (G472A), rs1611115 DBH (C-1021T), rs1800497 DRD2 (Taq1A).
The results of the study showed that orphans differ from children from full families in the following parameters: heredity burdened with alcoholism (p<0.001), pathology of the perinatal period (p<0.001), frequent occurrence of polymorphisms of the genes rs1800532 TPH1(A218C) (p=0.012) and rs4680 COMT (G472A) (p=0.009).
Statistically significant parameters formed the basis for training a multilayer perceptron. The use of neural network analysis made it possible to predict the development of mental disorders with an accuracy of at least 83.0%.
Keywords children left without parental care; social orphans; mental disorders; gene polymorphism; neural network analysis; multilayer perceptron
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DOI: http://dx.doi.org/10.34757/1560-957X.2023.27.5.006
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