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Fuzzy interpolation inference prediction method based on neighborhood rough set

To address the problems of ambiguity, incompleteness and small samples in some data, this paper proposes an evaluation model based on neighborhood rough set and fuzzy interpolation inference prediction, which uses neighborhood rough set to approximate the attributes of the data and construct a rule base. Then the data are processed by the fuzzy interpolation algorithm based on the center of gravity to obtain the final prediction evaluation value. Combining the attribute simplification of neighborhood rough sets with fuzzy interpolation inference enables the proposed system to handle such small samples as well as linguistic ambiguity. Finally, this model is applied to the coronary heart disease dataset, and by comparing it with a single fuzzy interpolation risk assessment model, it is shown that the proposed risk assessment model has higher accuracy.