Quantitative assessment of the contribution of risk factors to the formation of nutrition-dependent diseases based on neural network modelling in schoolchildren

Abstract

Introduction. The increased interest on the part of the state in the problem of healthy nutrition makes it urgent to develop methodological approaches to quantify the likelihood of the occurrence of nutrition-related diseases and assess health risks. Materials and methods. The source of information was data obtained from a sociological study conducted by the Federal Service for Supervision in Protection of the Rights of Consumer and Man Wellbeing (Rospotrebnadzor). For the analysis, there were used eleven thousand five hundred forty three questionnaires, characterizing the nutritional factors of schoolchildren in the Russian Federation in the regions. Associated relationships between the studied factors and morbidity were obtained through neural network modelling. The study of the contributions of factors to the formation of diseases was carried out based on the analysis of a simulation matrix containing 300 thousand possible nutrition scenarios. Results. In the course of mathematical modelling, there were delivered 12 neural network models that describe the dependence of the additional probability of developing nutrition-dependent diseases on nutritional factors, characterized by a high proportion of correct predictions (more than 70%). The contributions of factors to changes in the probability of developing obesity were analyzed as a model with a high degree of reliability of parameters. The factors most influencing the development of obesity have been established to include daily consumption of foods with a high glycemic index, high-calorie confectionery in the form of cakes and pastries, sweet juices, consumption of products from a vending machine, excluding the purchase of salads and vegetable dishes. This type of diet increases the likelihood of developing obesity from the original 0.033 to 0.98 for the average schoolchild. Limitations. The study is limited to a set of factors measured as a result of a sociological survey and used in training neural network models. Conclusion. The neural network models obtained as a result of the study and the information materials created on their basis made it possible to develop tools that make it possible to quickly create arbitrary nutrition scenarios for schoolchildren and calculate the additional probability of the formation of nutrition-related diseases.