Abstract
The application of soft computing technology for diagnosing the functional state of the cardiovascular system is considered. Soft computing technology uses fuzzy sets, fuzzy logic, fuzzy neural networks, genetic algorithms and evolutionary modeling as tools. Various methods of soft computing technology in solving various problems often complement each other when used in various combinations. This technology is focused on solving control problems with semi-structured control objects. The main informative indicators (indicator variables) characterizing the functional state of the cardiovascular system and obtained on the basis of statistical information are identified. These informative indicators include the tension index, the vegetative rhythm index, the indicator of the adequacy of regulatory processes, the tension index of regulatory systems, and also special indicators that are derivatives of classical statistical indicators: respiratory modulation index, functional arrhythmia index, cardiorespiratory synchrony index, parasympathetic control destabilization index. The quality of the classification of possible diseases is determined by indicators such as sensitivity, specificity, predictive value and diagnostic efficiency. Keywords: Neural networks, fuzzy inferences, diagnostic conclusion, confidence coefficient.
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