Based on prior knowledge, we can analyze the relationship between the feature "nr_04" (a persistent form of atrial fibrillation in the anamnesis) and the presence of chronic heart failure in myocardial infarction complications data.

We can hypothesize that patients with a persistent form of atrial fibrillation in their medical history (nr_04 = 'yes') are more likely to show chronic heart failure. On the other hand, patients without such a history (nr_04 = 'no') might have a lower likelihood of chronic heart failure.

To confirm this relationship, we would need to analyze a dataset that includes both the feature "nr_04" and the information about whether or not the patient has chronic heart failure.

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

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

In this case, the list of possible values of feature nr_04 for the target class "no" consists only of the single value "no", indicating that patients without a persistent form of atrial fibrillation do not show chronic heart failure. Similarly, the list of possible values of feature nr_04 for the target class "yes" consists only of the single value "yes", indicating that patients with a persistent form of atrial fibrillation do show chronic heart failure.

Please note that this analysis is based on general knowledge and assumptions. To obtain a more accurate relationship between the feature and the target, it would be necessary to analyze a specific dataset that includes relevant data for this task.