Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_12" and the presence of chronic heart failure in myocardial infarction complications data.

To analyze the relationship, we need to look at the distribution of "n_p_ecg_p_12" for each target class (yes or no for chronic heart failure).

Here is the dictionary with the details:

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

Since the feature "n_p_ecg_p_12" is a categorical variable with only two possible values (no and yes), we observe that "no" value corresponds to the target class "no" for chronic heart failure, and "yes" value corresponds to the target class "yes" for chronic heart failure.

Please note that for this specific feature, it seems that the possible values are already aligned with the target class, which makes it easy to predict the chronic heart failure outcome based on the "n_p_ecg_p_12" value. Therefore, there is no need to include other values in the dictionary, as they are not relevant for this relationship analysis.