Based on prior knowledge, it is important to analyze the relationship between the feature "n_r_ecg_p_03" and the presence of chronic heart failure in the myocardial infarction complications data.

To do this, we need to examine how the values of "n_r_ecg_p_03" are distributed among the target classes of "no" and "yes" for chronic heart failure.

Here is a detailed analysis of the relationship between the feature and task:

- For the target class "no" (indicating no chronic heart failure), the possible values of "n_r_ecg_p_03" could be:
  - "no": The patient did not have ventricular contractions on ECG at the time of admission to the hospital.
  - [Other values]: It is hard to predict what other specific values might indicate no chronic heart failure. But, it is important to note that the list of values should not be empty for the target class "no".

- For the target class "yes" (indicating chronic heart failure), the possible values of "n_r_ecg_p_03" could be:
  - "no": The patient had ventricular contractions on ECG at the time of admission to the hospital.
  - [Other values]: It is hard to predict what other specific values might indicate chronic heart failure. But, it is important to note that the list of values should not be empty for the target class "yes".

Based on this analysis, let's create the dictionary:

```json
{
	"no": ["no"],
	"yes": ["no"]
}
```
Please note that the dictionary contains the possible values for each target class, as described above.