There exist areas, such as the disease prevention or inclement weather protocols, in
which the analysis of the information based on strict protocols require a high level of rigor and
security. In this situation, it would be desirable to apply formal methodologies that provide these
features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures
two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the
models should be designed by domain experts, who have the required knowledge for the design of
the processes, but do not have the necessary technical knowledge. To address this limitation, this
paper proposes MODELFY, a novel model-driven solution for designing a decision-making process
based on fuzzy automata that allows users to abstract from technical complexities. With this goal
in mind, we have developed a framework for fuzzy automaton model design based on a Domain-
Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and
functionality of this framework, it also includes a model-to-text transformation that translates the
models designed by using the graphical editor into a format that can be used by a tool for data analysis.
The practical value of this proposal is also evaluated through a non-trivial medical protocol for
detecting potential heart problems. The results confirm that MODELFY is useful for defining such
a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain expert
