Technologies of Bioimpedance Spectroscopy in Decision Support Systems for the Diagnosis of Socially Significant Diseases

Abstract

The purpose of the research is to develop methods for the synthesis of hybrid classifiers to assess the risk of socially significant diseases using bioimpedance analysis.Methods. We developed a descriptor approach using impedance spectroscopy results, generating four amplitudephase-frequency responses from four quasi-orthogonal leads. They create the feature spaces necessary for our hybrid classifier in the diagnosis of pancreatic diseases, the autonomous intelligent agents of which are built on various paradigms: probabilistic neural networks, fuzzy logical inference, fully connected feedforward neural networks. We also presented a device structure for creating an informative feature space.Results. Experimental studies of the proposed methods and means of classifying medical rice were carried out on diagnostic tasks according to the classes "acute destructive pancreatitis" – "no acute destructive pancreatitis" and differential diagnosis tasks according to the classes "prostate cancer" ‒ "chronic pancreatitis". They showed that incorporating multi-frequency sensing into neural network-based classifiers allows the development of clinical decision support systems for disease diagnosis that are comparable in performance to existing clinical diagnostic methods. The results were confirmed in groups of male and female patients at different stages of cancer aged 25 to 80 years using a variety of diagnostic methods, including history, physical examination, assessment of comorbidities, laboratory tests, ultrasound, laparoscopy, intraoperative exploration and computed tomography.Conclusion. The use of bioimpedance spectroscopy and hybrid classifier models opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent medical decision support systems.