Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_08" and the target variable "chronic heart failure" to check if there is an association between them.

To perform the analysis, we will first gather the data on patients with myocardial infarction complications and note whether they have chronic heart failure or not. Then, we will examine the feature "n_r_ecg_p_08" for each group to see if there are any patterns or differences.

Let's assume we have collected the data and found the following information:

- For patients without chronic heart failure:
   - Possible values of feature "n_r_ecg_p_08" are: ['no', 'yes', 'unknown']

- For patients with chronic heart failure:
   - Possible values of feature "n_r_ecg_p_08" are: ['no', 'yes', 'unknown']

Based on this analysis, we can create the dictionary:

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

Please note that in this example, we do not have information on the values of "n_r_ecg_p_08" for patients with chronic heart failure that are hard to predict (e.g., 'unknown'). In such cases, it is not necessary to include those values in the dictionary, but we should ensure that each target class has at least one value in its list.