Based on prior knowledge, we can analyze the relationship between the feature "nr_08" (Ventricular paroxysmal tachycardia in the anamnesis) and the task of determining whether the patient has chronic heart failure.

To analyze this relationship, we can check the distribution of the "nr_08" values for the two target classes ('no' and 'yes') in the myocardial infarction complications data.

Let's assume we have the following sample data:

| nr_08 | Chronic Heart Failure |
|-------|-----------------------|
| no    | no                    |
| no    | no                    |
| yes   | yes                   |
| yes   | no                    |
| yes   | yes                   |
| no    | yes                   |

Based on this sample data, we can generate the following dictionary:

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

Note that the "nr_08" values "no" and "yes" are relevant to both target classes. The value "no" is associated with both the "no" and "yes" target classes, while the value "yes" is only associated with the "yes" target class. The "nr_08" value that is hard to predict (i.e., the one that appears in both target classes) is included in the dictionary.

Please note that this analysis is based on the assumption that the provided sample data is representative of the entire dataset. Additional analysis may be required with more data to make accurate predictions.