DIAGNOSTIC INDICATORS OF GENETIC FACTORS IN THE ABILITY TO CLASSIFY PATIENTS WITH CAROTID ARTERY REMODELING FROM CONDITIONAL Y HEALTHY ON THE BASIS OF MACHINE LEARNING

A model was obtained to differentiate between patients with carotid artery remodeling and conventionally healthy people based on machine learning using genetic data from the Kazakh population For the period 2018-2020, 561 study participants were recruited, of which 357 had PCA and 204 were conditionally healthy. Genotyping was performed using QuantStudio TM 12K Flex Real-Time PCR technology (Applied Biosystems) using a panel of 118 polymorphisms. After the quality control procedure, 43 polymorphisms were excluded from further analysis. The results of genotyping of 75 polymorphisms were used to build a binary classification model in the form of a «decision tree» based on the Fast-and-frugal trees (FFTs) algorithm. The best model of 5 polymorphisms had 52% sensitivity, 64% specificity, and 56% accuracy in the ability to distinguish between RSA patients and conventionally healthy people based on genetic data.

carotid artery remodeling, machine learning, classification model, decision tree

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