Predicting coronary heart disease in locomotive crew employees based on hybrid fuzzy models

The aim of the study is to improve the quality of predicting coronary heart disease in railway locomotive crews by developing hybrid fuzzy mathematical models that work under conditions of incomplete and fuzzy description of the object of research. Taking into account the poorly formalized structure of the studied class of States, the technology of soft computing and, in particular, the methodology for the synthesis of hybrid fuzzy decision rules, which has proven itself well in solving problems with a similar data structure and type of uncertainty, is chosen as the basic mathematical apparatus. The chosen synthesis method allows us to take into account the multiplicative effect of heterogeneous and unstable endogenous and exogenous risk factors on the human body in the locomotive cabs. The obtained mathematical models for predicting ischemic heart disease in locomotive crew workers take into account cabin ergonomics, levels of psycho-emotional stress and fatigue, mixed electromagnetic fields in combination with individual risk factors for systemic ischemic damage as initial data. In the course of mathematical modeling and expert evaluation, it was shown that the obtained predictive model provides confidence in the correct forecast of at least 0.89, which is a fairly "good" result for medical diagnostics tasks.

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