Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_04" and the target variable "chronic heart failure" (CHF).

In order to determine if the myocardial infarction complications data of a patient show chronic heart failure (CHF), we need to analyze how the "n_r_ecg_p_04" feature is related to CHF.

From the feature description, "n_r_ecg_p_04" represents frequent premature ventricular contractions on an electrocardiogram (ECG) at the time of admission to the hospital. We can assume that if a patient has frequent premature ventricular contractions recorded on the ECG, it may indicate an abnormal heart rhythm and potentially increase the risk of chronic heart failure (CHF).

To complete the analysis, we would require the actual data and the target variable values (CHF data). By examining the data, we could check if patients with "yes" value for the "n_r_ecg_p_04" feature have a higher incidence of chronic heart failure compared to those with "no" value for the feature. 

Then, we would generate a dictionary with the possible values of the feature "n_r_ecg_p_04" for each target class (CHF "yes" and "no") using the format mentioned:

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

Please note that this is a hypothetical example and the actual analysis and dictionary generation would require access to the specific data for accurate results.