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In this article, fuzzy set membership functions have been developed based on the main factors (clinical, morphochemical, rumen contents) to predict the following non-communicable diseases such as ketosis, osteodystrophy, secondary osteodystrophy and hypomicroelementosis in high-yielding cows. The results of the study show the high efficiency of the proposed decision-making algorithm for forecasting, classifying and measuring poorly formalized processes, that are described by fuzzy models. The available knowledge about the existing experimental data makes it possible to increase the adequacy of the fuzzy expert system.

The fuzzy integral of a function with a fuzzy measure is a general estimate in the form of a nonlinear convolution of particular estimates of information elements, and the possibility of interconnection of information elements is not excluded. Here is an approach that allows the expert to best formalize his fuzzy ideas, transforming the language of words into a language of quantitative estimates.