Based on my prior knowledge, I can analyze the relationship between the feature "Observing of arrhythmia in the anamnesis" (nr_11) and the presence of chronic heart failure in patients with myocardial infarction complications.

Based on this feature, there are two possible categories: "no" and "yes". To determine if this feature indicates chronic heart failure, we need to analyze the distribution of these categories for each target class.

To do this, we need access to a dataset that includes the myocardial infarction complications data for patients and their corresponding presence or absence of chronic heart failure. We can then analyze the relationship between the feature and the target variable to determine if there is a strong association.

Once we have the dataset, we can create a dictionary that includes the possible values of the feature "Observing of arrhythmia in the anamnesis" (nr_11) for each target class. For example:

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

Note: This example dictionary includes only the values "no" and "yes" for each target class. If there are additional values for the feature "nr_11" that are relevant to determining chronic heart failure, they should be included in the respective target class list in the dictionary.