Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields.

The study aims to enhance the standard of medical care for individuals working in the electric power industry who are exposed to industrial frequency electromagnetic fields and other relevant risk factors. This enhancement is sought through the integration of fuzzy mathematical models with contemporary information and intellectual technologies. The study addresses the challenges of forecasting and diagnosing illnesses within a specific demographic characterized by a combination of poorly formalized issues with interconnected conditions.

Development of a Fuzzy Diagnostic Model of Ischemic Disease of the Lower Limbs for Different Stages of Patient Management.

Ischemic disease has severe impact on patients which makes accurate diagnosis vital for health protection. Improving the quality of prediction of patients with ischemic extremity disease by using hybrid fuzzy model allows for early and accurate prognosis of the development of the disease at various stages. The prediction of critical ischemia of lower extremity (CLI) at various disease stages is complex problem due to inter-related factors.

Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology.

Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range.

Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points.

Objective: This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery.

Methods: A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors.