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

Since "fibr_ter_08" represents the use of fibrinolytic therapy by Streptodecase, it is not directly related to chronic heart failure. However, we can still analyze the distribution of this feature for patients with and without chronic heart failure to see if there are any significant patterns.

To analyze the relationship, we would need data for patients who have experienced myocardial infarction complications and information about whether they have chronic heart failure.

Once we have the data, we can divide it into two groups based on the presence or absence of chronic heart failure and then examine the distribution of the "fibr_ter_08" feature within each group. We will check the possible values of "fibr_ter_08" for patients with chronic heart failure and those without it.

Based on the analysis, we can create a dictionary with the possible values of "fibr_ter_08" for each target class. Since the feature is categorical, we will list the possible values as strings within lists. If there are values that are hard to predict or not present in the data for a particular target class, we can exclude them from the dictionary.

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

Please note that the dictionary above assumes that "fibr_ter_08" can have two possible values, "no" and "yes", for both target classes. If there are additional possible values for the feature, they should be included in the corresponding target class list.