Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_08" and the task of predicting whether the patient has chronic heart failure.

To analyze this relationship, we need to examine the distribution of the feature values for each target class (whether the patient has chronic heart failure or not).

Here is the analysis of the relationship between "fibr_ter_08" and chronic heart failure:

Target Class: No (chronic heart failure is not present)
- Possible values of "fibr_ter_08": 'no', 'yes'

Target Class: Yes (chronic heart failure is present)
- Possible values of "fibr_ter_08": 'no', 'yes'

Based on this analysis, we can create the dictionary with the format requested:

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

Please note that in this case, the possible values of "fibr_ter_08" are the same for both target classes. If there were additional values for the feature that were hard to predict, we could exclude them from the dictionary.