Based on the given feature description and task, the goal is to analyze the relationship between the feature "fibr_ter_02" (Fibrinolytic therapy by Celiasum 1m IU) and the presence of chronic heart failure in a patient.

To analyze this relationship, we need a dataset that contains the fibr_ter_02 values and the corresponding information on whether the patient has chronic heart failure or not. Ideally, this dataset would consist of multiple observations for different patients.

Assuming we have access to such a dataset, we can examine the distribution of fibr_ter_02 values for the two target classes (chronic heart failure (CHF) present and CHF not present) and determine the relationship.

Here's an example analysis of the relationship between "fibr_ter_02" and chronic heart failure:

| fibr_ter_02 | CHF Present | CHF Not Present |
|-------------|-------------|----------------|
| no          | high        | high           |
| yes         | low         | high           |

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

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

Explanation of the dictionary:

- For the target class "no" (CHF not present), the possible values of "fibr_ter_02" are ["yes"].
- For the target class "yes" (CHF present), the possible values of "fibr_ter_02" are ["yes", "no"].

This indicates that patients with chronic heart failure (CHF) can have either a "yes" or "no" value for the feature "fibr_ter_02". On the other hand, patients without CHF (no) only have the value "yes" for "fibr_ter_02".

Please note that this analysis and resulting dictionary are based on hypothetical data. The actual relationship may vary depending on the dataset used for analysis.