Abstract

The purpose of research is to improve the quality of predicting and diagnosing diseases caused by the action of heterogeneous risk factors by using the estimated characteristics of the body's protection level, determined by the energy characteristics of biologically active points that form the basis of the corresponding meridian structures.Methods. Explores the issues of using indicators characterizing the energy characteristics of meridian structures for a quantitative assessment of the level of protection of the body from the effects of many diverse environmental factors. Since the indicators under study have a pronounced fuzzy nature, the methodology for synthesizing hybrid fuzzy decision rules was chosen as the basic mathematical apparatus.Results. The paper shows that for a quantitative assessment of the level of protection of the body at its various levels (organism, system, organ), it is advisable to use the imbalance of the electrical resistance of the corresponding biologically active points from their nominal values, determined under normal conditions and after a dosed load, as the energy characteristics of the meridian structures. A method for assessing the level of protection by the energy imbalance of the meridian structures of the body is proposed, which differs in that the energy imbalance of the meridian structures for the selected level of research in combination with the load energy imbalance is used as the basic variable functions of the protection level (PLF), which allows assessing the level of protection of the body as a whole, as well as its systems and organs with an accuracy acceptable for medical practice. The ways of embedding FUS in predictive and diagnostic decision rules are shown.Conclusion. In the course of the studies, it was shown that in order to improve the quality indicators of forecasting and diagnosing socially significant and occupational diseases, it is advisable to use indicators of the level of body protection determined by the energy imbalance of meridian structures. It is shown that the quality of decision-making with the use of FLS increases by 10–20% depending on the type of tasks being solved and the completeness of the collected data compared to traditionally obtained models.

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