A description of the mathematical model of a neural network classifier of data on healthcare in the institutions of the Lipetsk region is given in order to identify atypical (abnormal) records. Anomaly detection refers to the problem of finding data that is inconsistent with some expected process behavior or metric occurring in the system. Due to the large number of inputs to the neural network model, the time it takes to process the incoming information also increases. To assess what factors should be transmitted to the input of the neural network classifier, an approach to the reduction of the neural network model based on sensitivity analysis is proposed. The description of a set of software tools for solving the problem is presented.