Based on the given feature "nr_03", which represents the presence of paroxysms of atrial fibrillation in the anamnesis, we can analyze its relationship with the target variable of chronic heart failure in myocardial infarction complications data.

To determine if the patient shows chronic heart failure, we need to investigate the possible values of the feature "nr_03" for both classes, "yes" and "no".

By analyzing historical data and domain knowledge, we can make assumptions about the relationship between the feature and the target:

1. If the patient has a history of paroxysms of atrial fibrillation (nr_03 = "yes"), it is possible that they have developed chronic heart failure as a complication of myocardial infarction. Atrial fibrillation is known to increase the risk of heart failure in patients with a history of myocardial infarction.
2. If the patient has no history of paroxysms of atrial fibrillation (nr_03 = "no"), it is less likely that they have developed chronic heart failure as a complication. Atrial fibrillation is a known risk factor for heart failure, and its absence reduces the likelihood of heart failure.

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

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

In this case, the feature "nr_03" has only two possible values, "no" and "yes". The associated possible values for the target class "no" are ["no"], indicating that patients without a history of paroxysms of atrial fibrillation are less likely to have chronic heart failure. And for the target class "yes", the associated possible values are ["yes"], indicating that patients with a history of paroxysms of atrial fibrillation are more likely to have chronic heart failure.