Based on the given feature and task, we need to analyze the relationship between the feature "fibr_ter_03" and the presence of chronic heart failure in the myocardial infarction complications data. 

To do this analysis, we can look at the distribution of "fibr_ter_03" values for both classes, "yes" and "no". Here is an example analysis:

For the target class "no" (no chronic heart failure):
- Possible values of feature "fibr_ter_03" could be ['no', 'yes']
- It is possible that patients without chronic heart failure received no fibrinolytic therapy ("no" value for "fibr_ter_03").
- It is also possible that patients without chronic heart failure received fibrinolytic therapy ("yes" value for "fibr_ter_03").

For the target class "yes" (chronic heart failure):
- Possible values of feature "fibr_ter_03" could be ['no', 'yes']
- It is possible that patients with chronic heart failure received no fibrinolytic therapy ("no" value for "fibr_ter_03").
- It is also possible that patients with chronic heart failure received fibrinolytic therapy ("yes" value for "fibr_ter_03").

Based on this analysis, we can create the following dictionary:

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

This dictionary shows the possible values of the feature "fibr_ter_03" for both target classes "no" (no chronic heart failure) and "yes" (chronic heart failure).