Prediction of hormonal and metabolic disorders in young women with overweight and obesity: the effectiveness of artificial neural networks
Background. The relevance of the study is due to the need to create methodological approaches to the formation of risk groups for the development of metabolic syndrome - the basis of chronic non-infectious pathology of the cardiovascular system in young women with excess body weight of varying degrees. The purpose was to create models for predicting the development of metabolic syndrome in young women with overweight and obesity - representatives of the Ukrainian population using classical methods of statistical analysis (discriminant analysis, logistic regression) and artificial neural networks and comparative analysis of the prognostic accuracy of the created models. Materials and methods. One hundred and thirty women with average age of 28.64 ± 6.91 years were examined. They had exogenous constitutional excess body weight of varying degrees. Body mass index and waist circumference were determined. The method of bioimpedance analysis was used to evaluate body composition (fat, relative fat, fat-free, active cell body mass); enzyme immunoassay - to determine the levels of insulin- and leptinemia. The HOMA-IR was calculated. The secretion of melatonin was assessed by the level of its metabolite 6-sulfatoxymelatonin in urine by Druex method modified by G.V. Zubkov; serotonin - by the fluorimetric method of V.I. Kulinsky and L.V. Kostyukovska. The presence of sleep and eating disorders was assessed by questionnaires. Results. According to the results of a comprehensive laboratory and instrumental examination using methods of discriminant analysis, logistic regression and artificial neural networks, four mathematical models were created that allow us to assess the risk of metabolic syndrome in young women with excess body weight of varying degrees - representatives of the Ukrainian population by the levels of anthropometric indicators, parameters of body composition, carbohydrate and lipid metabolism, indicators that characterize the features of melatonin secretion. Conclusions. Diagnostic characteristics of all proposed models (sensitivity, specificity, accuracy, odds ratio) are quite high, but the greatest diagnostic informativeness is determined for the model using artificial neural networks built in the program Statistica StatSoft. The use of such a model as a tool for determining the risk of developing the metabolic syndrome in specific young women with excess body weight of varying degrees in the practical healthcare system will improve the risk-stratification of the metabolic syndrome and provide timely therapy to prevent its complications.