Based on the task description, we want to analyze whether the feature "nr_04" (a persistent form of atrial fibrillation in the anamnesis) is related to the presence of chronic heart failure in a patient.

To do this, we can examine the distribution of "nr_04" values for patients with and without chronic heart failure. Let's assume the target variable for chronic heart failure is denoted as "CHF" with values of "yes" and "no".

Here is the analysis and resulting dictionary:

- For patients with chronic heart failure (target variable = "yes"):
  - As the feature "nr_04" represents a persistent form of atrial fibrillation in the anamnesis, it is likely that patients with chronic heart failure may have a higher occurrence of this condition. Therefore, we can expect that the value "yes" in "nr_04" may be more frequent for patients with chronic heart failure.
  - Other possible values of "nr_04" for patients with chronic heart failure cannot be determined without additional information.
- For patients without chronic heart failure (target variable = "no"):
  - Patients without chronic heart failure may not necessarily have a persistent form of atrial fibrillation in the anamnesis. Therefore, the value "no" in "nr_04" may be more common for patients without chronic heart failure.
  - Other possible values of "nr_04" for patients without chronic heart failure cannot be determined without additional information.

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

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

Please note that the specific values for "nr_04" cannot be predicted accurately without further information. However, we include all the possible values for each target class to ensure the lists are not empty.