Based on prior knowledge, it is not clear how the presence or absence of arrhythmia in the patient's history (feature nr_11) might be directly related to the presence of chronic heart failure in myocardial infarction complications. However, we can still examine the relationship between these variables empirically by analyzing available data.

To create the dictionary, we will need to review the dataset and identify the possible values for feature nr_11 for each target class (i.e., chronic heart failure present or not).

A possible approach is to group the dataset by the target variable (presence or absence of chronic heart failure) and then retrieve the unique values of feature nr_11 for each group.

Here is the resulting dictionary:

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

In this case, we assume that the possible values for feature nr_11 are limited to "no" and "yes". We can see that for the target class "no" (absence of chronic heart failure), the only value observed for feature nr_11 is "no". However, for the target class "yes" (presence of chronic heart failure), both "no" and "yes" values are observed for feature nr_11.