Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_06" and the target variable "chronic heart failure" in patients with myocardial infarction complications.

Since the feature represents the presence or absence of persistent form of atrial fibrillation on ECG at the time of admission to the hospital, we can assume that patients with a positive value ("yes") for this feature might have a higher likelihood of chronic heart failure. On the other hand, patients with a negative value ("no") for this feature might have a lower likelihood of chronic heart failure.

Based on this analysis, we can create the dictionary as follows:

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

In this case, since the feature is categorical with only two possible values ("no" and "yes"), we can directly map each target class (no and yes) to their respective values for the feature.