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

To do this, we need data on patients who have experienced myocardial infarction complications and their corresponding information on fibrinolytic therapy. By analyzing this data, we can determine whether there is any association between the "fibr_ter_08" feature and the presence or absence of chronic heart failure.

Here is the analysis of the relationship between the feature and the target:

- If the value of "fibr_ter_08" is "no," it indicates that the patient did not receive fibrinolytic therapy by Streptodecase 1.5m IU.
    - If there is no chronic heart failure, then the value of the target variable would also be "no."
    - If there is chronic heart failure, then the value of the target variable may be either "yes" or "no." This depends on other underlying factors and cannot be solely determined by the presence or absence of fibrinolytic therapy.

- If the value of "fibr_ter_08" is "yes," it indicates that the patient received fibrinolytic therapy by Streptodecase 1.5m IU.
    - If there is no chronic heart failure, then the value of the target variable would be "no."
    - If there is chronic heart failure, then the value of the target variable would be "yes."

Based on this analysis, we can create a dictionary with the possible values of the "fibr_ter_08" feature for each target class:

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

It's important to note that since the "fibr_ter_08" feature has only two categories, "no" and "yes," we include these two categories in the dictionary. However, for the target class "no," we only have the value "no" for the feature. For the target class "yes," we have the value "yes" for the feature.