Based on the given feature "fibr_ter_02" and the task to determine if the patient's myocardial infarction complications data shows chronic heart failure, we can analyze the relationship between the feature and the target variable.

Since the feature "fibr_ter_02" represents whether fibrinolytic therapy by Celsium 1m IU was used or not, we can examine its relationship with chronic heart failure. 

To analyze this relationship, we need to gather information on whether patients who received fibrinolytic therapy by Celsium 1m IU (yes category) tend to have chronic heart failure compared to those who did not receive this therapy (no category).

Based on prior knowledge, we can't directly infer a relationship between fibrinolytic therapy and chronic heart failure. Fibrinolytic therapy is generally administered to dissolve blood clots and does not directly address chronic heart failure, which is a long-term condition involving the inability of the heart to pump blood effectively.

To create the requested dictionary, it is not necessary to include the fibr_ter_02 values that are hard to predict, as long as both target classes have a non-empty list. 

Thus, the resulting dictionary would be:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```

This dictionary indicates that the category of "no" in the fibr_ter_02 feature is associated with the target class "no" (indicating absence of chronic heart failure), and the category of "yes" in the fibr_ter_02 feature is associated with the target class "yes" (indicating presence of chronic heart failure).