Based on my prior knowledge, premature atrial contractions (PACs) on an electrocardiogram (ECG) can be an indication of certain heart conditions, including chronic heart failure. Therefore, there may be a relationship between the feature n_r_ecg_p_01 (premature atrial contractions on ECG at the time of admission to the hospital) and the presence of chronic heart failure in the myocardial infarction complications data.

To analyze this relationship, we can examine the values of n_r_ecg_p_01 for each target class (whether the patient shows chronic heart failure or not) and see if there are any patterns.

Here is the analysis:

For target class "no" (patients without chronic heart failure):
- The feature n_r_ecg_p_01 may have the value "no" or "yes" since PACs can occur in patients without chronic heart failure as well.
- Other possible values that are hard to predict may exist, but we do not need to include them in the dictionary as long as the list of values for this target class is not empty.

For target class "yes" (patients with chronic heart failure):
- The feature n_r_ecg_p_01 may have the value "no" or "yes" indicating the presence of PACs.
- Possible values other than "no" or "yes" may exist, but we do not need to include them in the dictionary as long as the list of values for this target class is not empty.

Based on this analysis, we can create the following dictionary:

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

In this dictionary, the key "no" represents the target class without chronic heart failure, and the key "yes" represents the target class with chronic heart failure. The corresponding values for each target class are the possible values of the feature n_r_ecg_p_01.