Based on the given feature and task, we want to analyze if there is a relationship between the fibr_ter_06 feature (Fibrinolytic therapy by Celiasum 500k IU) and the presence of chronic heart failure in the patient.

To conduct the analysis, we can compare the distribution of the fibr_ter_06 feature for patients with and without chronic heart failure. We can then identify the possible values of fibr_ter_06 for each target class.

After the analysis, we can create a dictionary with the required format, where the keys represent the target classes (yes and no) and the values are lists of possible values of fibr_ter_06 specific to each target class.

Here is the analysis and the resulting dictionary:

Analysis:

- For patients with chronic heart failure (target class: yes):

  Possible values of fibr_ter_06 could be:

  - 'no': Patients with chronic heart failure might not have received Fibrinolytic therapy with Celiasum 500k IU.

- For patients without chronic heart failure (target class: no):

  Possible values of fibr_ter_06 could be:

  - 'yes': Patients without chronic heart failure might have received Fibrinolytic therapy with Celiasum 500k IU.

Based on this analysis, we can create the dictionary:

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

This dictionary indicates that patients without chronic heart failure (no) can have a possible value of "yes" for the feature fibr_ter_06, while patients with chronic heart failure (yes) can have a possible value of "no" for the feature fibr_ter_06.