Intelligent analysis of anthropometric data in the treatment of obesity in children
The problem of building information models for the prevention and treatment of obesity is considered. A sample of anthropometric data in children is used as a material for the study. To search for hidden patterns in the data (qualitative and quantitative), intellectual analysis based on classification methods was used. The classification was carried out according to the descriptions of the patients before and after treatment. The question of the limited possibilities of extracting knowledge from raw data represented by a set of admissible values of features was investigated.In order to describe objects in the new space, a nonlinear transformation of features was used through the function of belonging to classes of objects. Taking into account the classification of objects by the values of the membership function, estimates of the stability of the features were calculated, invariant to the scales of their measurements. Trait weights were used to assess the effectiveness of treatment for obesity. Hidden patterns were found when comparing the weights of raw features and those obtained by nonlinear transformation.The revealed regularities make it possible to increase the objectivity of decisions taken in the treatment of patients.