The article examines a method for constructing an expert system based on frames. Special emphasis is placed on accounting for fuzziness/uncertainty in the interpretation of linguistic variables, which is highly relevant in situations where data is ambiguous, i.e., it may indicate different stages of a disease or are in the boundary area for classified states. To solve this problem, a method is proposed that allows to form and output hypotheses to the user about contiguous situations, taking into account the presence of fuzzy initial data. The described approach is especially important for poorly structured subject areas, in example for medical diagnostics, where the accuracy of interpretation of indicators directly affects the quality of decisions. In addition, it is important that the processing of linguistic variables makes the system more intuitive for the doctor and, accordingly, increases the confidence in it when it is introduced into practice. The system is tested on the task of diagnosing the stages of progression of Duchenne muscular dystrophy, which belongs to rare diseases. The advantage of the proposed approach is its universality, it can be adapted for other medical tasks in which there is uncertainty in data and decision making. The results of the study may also be useful for the development of knowledge-based systems in other subject areas where uncertainty in data occurs.