Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_06" and the task of determining whether the patient has chronic heart failure.

To analyze this relationship, we need to look at the distribution of the "fibr_ter_06" feature for both the "yes" and "no" classes of the target variable.

Here is the analysis:

- For patients with chronic heart failure (target class "yes"), the possible values of the "fibr_ter_06" feature could be ['no', 'yes'].

- For patients without chronic heart failure (target class "no"), the possible values of the "fibr_ter_06" feature could also be ['no', 'yes'].

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

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

This dictionary represents the possible values of the "fibr_ter_06" feature for each target class.