Analysis:

To determine the relationship between the feature "fibr_ter_06" and the presence of chronic heart failure, we can analyze the distributions of the feature values for both the positive and negative cases of chronic heart failure.

Here are the steps to conduct the analysis:

1. Split the dataset into two groups based on the target variable:
   - Group 1: Patients with chronic heart failure (target class "yes").
   - Group 2: Patients without chronic heart failure (target class "no").

2. For each group, identify the unique values of the feature "fibr_ter_06" and record them.

3. Determine the frequency distribution of the "fibr_ter_06" values within each group.

4. Analyze the distribution to see if certain values of "fibr_ter_06" are more prevalent in either target class.

5. Based on the analysis, create the dictionary with the requested format.

Based on prior knowledge, it is expected that the majority of patients with chronic heart failure (target class "yes") would not have received fibrinolytic therapy (fibr_ter_06 = 'no'), as this condition typically requires interventions beyond fibrinolysis.

Dictionary:

Based on the analysis, let's create the dictionary:

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

Since the feature "fibr_ter_06" is a binary categorical variable, it only has two possible values: 'no' (indicating no fibrinolytic therapy) and 'yes' (indicating fibrinolytic therapy with Celiasum 500k IU). In this case, the feature values are straightforward and no additional options are available. Therefore, we can directly include these values in the dictionary.