Based on prior knowledge, we can analyze the relationship between the feature "nr_03" (Paroxysms of atrial fibrillation in the anamnesis) and the task of determining whether the myocardial infarction complications data of a patient show chronic heart failure.

Since the feature "nr_03" represents whether the patient has experienced paroxysms of atrial fibrillation in their medical history, it is possible that patients who have experienced these paroxysms are more likely to develop chronic heart failure. However, without access to specific data or further evidence, it is difficult to determine the exact relationship between this feature and the target variable.

Therefore, we will create a dictionary as requested, but we will include all possible values of feature "nr_03" for each target class, regardless of our ability to predict their relationship to chronic heart failure.

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

In the above dictionary, we assume that the possible values for feature "nr_03" are "no" and "yes." We include both values for each target class ("no" and "yes") to ensure that the list of each target class is not empty.