Based on prior knowledge, we can analyze the relationship between the feature "nr_04" and the task of determining whether the patient has chronic heart failure.

Since "nr_04" represents a persistent form of atrial fibrillation in the patient's medical history, it is likely that individuals with a history of atrial fibrillation would have a higher chance of developing chronic heart failure. Therefore, it is expected that the feature values of "yes" (indicating a persistent form of atrial fibrillation in the anamnesis) would be more common among patients with chronic heart failure.

To generate the dictionary, we need to examine the relationship between the feature and the target variable by analyzing the data. Based on the provided information, we can group the feature values of "nr_04" into two categories: "no" and "yes". We will then analyze these categories separately to identify the possible values of "nr_04" for each class.

Here is the dictionary based on the provided information:

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

In this case, we only have binary options for the feature "nr_04" ('no' and 'yes'). The values in the dictionary represent the possible values of "nr_04" for each target class. The target class "no" corresponds to patients without chronic heart failure and the target class "yes" corresponds to patients with chronic heart failure.

Note: If there are additional data or alternative values for the feature "nr_04" that were not provided, they can be included in the dictionary accordingly.