Quinlans https://en.wikipedia.org/wiki/C4.5_algorithm is somewhat popular for similar tasks, it allows to build decision trees from data that are conceptually similar to such fuzzy rules, and the rules can be human-readable so they can be really powerful after expert review.

For example, a very specific condition can either mean that this particular condition is useful, or that simply the training data happened to have those particular instances of a more general condition - and a human expert can usually easily decide which rules need to be extended for proper generalization beyond your training data, but the automated generation helps identify factors that the expert could recognize, but wouldn't think of if doing it themselves from scratch.