Abstract
Introduction. Periodontal tissue disease is one of the most common dental pathologies, which among young people occurs with a frequency of 60% to 99%. Therefore, the problem of finding new links in the pathogenesis, the reasons for the growing prevalence of periodontal disease, as well as effective methods for its early diagnosis and prevention, is relevant. Objectives. Establish the possibility of using individual stomatological and psychophysiological features to predict the development of periodontal disease. Materials and methods. 156 students aged 18-23 years old without systemic diseases were surveyed for some features of oral hygiene and nutrition. Also the study subjects underwent a dental examination, psychological testing and the assessment of individual typological features of higher nervous activity and autonomous regulation. The model for statistical prediction were designed using neural networks. Results. Two neural networks were designed with the best predictors among dental history and examination, psychological testing, parameters of higher nervous activity and heart rate variability analysis. The diagnostic sensitivity of the first prognostic model was 83.33 % and the specificity was 92.31 %. The second model was characterized by 90.00 % sensitivity and 78.57 % specificity. Conclusion. The method of modeling using neural networks based on the index assessment of the condition of teeth hard tissues, the level of oral hygiene and the evaluation of psychophysiological features can effectively predict the risk of periodontal disease development in young people