Evaluation of the effectiveness of training and the use of neural networks to predict biological age.

Abstract

Neural network training is widely used in various educational fields: staff training, mastering the curriculum at school and university, forming recommendations for private use by respondents, for teaching pensioners health-saving techniques. It is relevant to analyze the learning process of neural networks and evaluate their effectiveness on various models. For the study, a model was chosen for predicting the index of biological aging of a person based on the characteristics of his personality. To train neural networks, a data matrix of 1,632 people aged 35 to 70 years was compiled. Output variable: biological aging index; input variables: gender, age, family and professional status, place of residence, body type, type of functional asymmetry, style of relationships with people, as well as personal resources. Four neural networks were trained: for men and women, for working professionals and for pensioners. Results: 1) trained neural networks give different recommendations for men and women of pre-retirement and post-retirement age. 2) The effectiveness of predicting the biological aging index using neural networks turned out to be significantly high for all trained programs. 3) Neural networks can be used to model various social situations and identify what changes this will lead to for output variables. Situations were modeled: a) if all single adults get married, b) if all family adults break up, c) if everyone receives the recommendations of psychologists on the selection of personal resources and will use them. The neural network has issued a forecast: it is better for adult women not to change their family status/it is better for adult men to change their status. The use of personal resources selected by psychologists is effective for everyone.