Based on prior knowledge, we can analyze the relationship between the feature "nr_11" (Observing of arrhythmia in the anamnesis) and the presence of chronic heart failure (target variable) in patients.

First, let's assume that if there is no observation of arrhythmia in the anamnesis (nr_11 = 'no'), there is a higher likelihood that the patient does not have chronic heart failure. On the other hand, if there is a history of arrhythmia (nr_11 = 'yes'), there might be a higher possibility of chronic heart failure in the patient.

With this assumption, we can create the following dictionary:

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

Explanation:
- If the feature value "nr_11" is 'no', it indicates that there was no observation of arrhythmia in the anamnesis. In this case, we can infer that the patient does not have chronic heart failure.
- If the feature value "nr_11" is 'yes', it indicates that there was a history of arrhythmia. In this case, there might be a higher likelihood of chronic heart failure in the patient.

Please note that this is a simplified analysis and assumption based on prior knowledge. For more accurate results, it is recommended to perform a comprehensive statistical analysis using appropriate modeling techniques.