Neural fuzzy networks for remote monitoring systems for outpatients with respiratory diseases

The article proposes a decisive module for monitoring the functional state of the respiratory system, which provides intellectual support in making decisions by medical personnel regarding the hospitalization of a patient. To control the severity of community-acquired pneumonia, a hybrid multi-agent classifier has been developed based on Internet technologies with a structure that includes segments of risk factors associated with “its own” fuzzy inference system. A metaclassifier has been designed to aggregate the solutions of these systems, which allows monitoring the functional state of the patient breathing system in remote interactive mode. Based on the Mamdani-Larsen algorithm, a five-layer fuzzy network has been developed for classifying the severity of community-acquired pneumonia according to the input vector, which allows estimating the severity of community-acquired pneumonia on a 0–1 scale according to the segment of risk factors used in traditional pneumonia risk scales. A neuro-fuzzy classifier of community-acquired pneumonia severity based on the CRB-65 pneumonia risk scale was synthesized. The base of fuzzy decision rules of the fuzzy inference system is formed and the membership functions for input and output variables in the selected segment of risk factors are determined. The neuro-fuzzy model of a hybrid classifier of the severity of community-acquired pneumonia was tested using an experimental group of 200 patients with community-acquired pneumonia of varying severity. The classifier model on the control sample demonstrated a diagnostic sensitivity of 90 % and diagnostic specificity of 86 %. The results of the obtained risk model for community-acquired pneumonia were compared with the results of expert evaluation and the results obtained on known regression models. The quality indicators of the classification of the synthesized neuro-fuzzy classifier make it possible to recommend it for telecommunication systems for remote monitoring of community-acquired pneumonia severity.

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