Based on the given feature description and task, the feature nr_04 represents the presence or absence of a persistent form of atrial fibrillation in the patient's medical history. To determine whether this feature is related to the presence of chronic heart failure as a myocardial infarction complication, we need to analyze the relationship between nr_04 and the target variable, chronic heart failure.

Here are the possible scenarios and their interpretations:

1. Nr_04 = 'no' and Chronic Heart Failure = No:
   This indicates that the patient does not have a persistent form of atrial fibrillation in the medical history and does not exhibit chronic heart failure. This scenario supports that the myocardial infarction complications data of this patient does not show chronic heart failure.

2. Nr_04 = 'no' and Chronic Heart Failure = Yes:
   This scenario suggests that the patient does not have a persistent form of atrial fibrillation in the medical history but does exhibit chronic heart failure as a myocardial infarction complication. This contradicts the assumption that the presence of nr_04 indicates chronic heart failure.

3. Nr_04 = 'yes' and Chronic Heart Failure = No:
   In this case, the patient has a persistent form of atrial fibrillation in the medical history, but chronic heart failure is not present. This scenario supports that the myocardial infarction complications data of this patient does not show chronic heart failure.

4. Nr_04 = 'yes' and Chronic Heart Failure = Yes:
   This scenario suggests that the patient has a persistent form of atrial fibrillation in the medical history and also exhibits chronic heart failure as a myocardial infarction complication. This supports the assumption that the presence of nr_04 indicates chronic heart failure.

To summarize the relationships between the feature nr_04 and the target variable chronic heart failure:

- If nr_04 = 'no', the patient may or may not have chronic heart failure. 
- If nr_04 = 'yes', the patient is more likely to have chronic heart failure.

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

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

This dictionary signifies that for the target class "no" (i.e., chronic heart failure is not present), the possible value(s) for the feature nr_04 is "no". Similarly, for the target class "yes" (i.e., chronic heart failure is present), the possible value(s) for the feature nr_04 is "yes".