Artifcial intelligence capabilities in evaluating effectiveness of non-medicinal treatment of obesity in children

Abstract

Introduction. Non-drug therapy for obesity cannot always guarantee a positive result, which forces doctors and scientists from all over the world to look for new methods for analyzing the effectiveness of treatment, including using artifcial intelligence. Its active implementation can significantly improve the quality of diagnosis and prognosis of the disease. Purpose of the study. To evaluate the possibilities of using the artifcial intelligence system in predicting the effectiveness of non-drug therapy for obesity in children.Materials and methods. An artifcial neural network was built using the Statistica Neural Networks software package based on data from patients who were hospitalized at the Voronezh Children's Clinical Hospital of the VSMU n.a. N.N. Burdenko.Results. The study group included 60 children (30 boys and 30 girls), aged 8 to 16 years. We selected the parameters that, in our opinion, have the most signifcant impact on the effect of non-drug treatment of obesity: the presence and frequency of inpatient treatment; obesity complications; compliance with the regime of physical activity and dietary recommendations; dynamics of body weight during non-drug treatment. After training, the neural network MLP 5-5-1 was selected with determination coeffcients of 0.925231; 0.981940; 0.936712 for training, test and control samples. The learning error is 0.105782, the learning algorithm is BFGS. The activation function of hidden neurons is hyperbolic, and the output function is identical.Conclusion. The results of the study show that an artifcial neural network can be used to evaluate the effectiveness of non-drug treatment with a minimum error.