Based on prior knowledge, premature atrial contractions on an ECG (n_r_ecg_p_01) can be an indicator of chronic heart failure. To determine if the myocardial infarction complications data shows chronic heart failure, we can analyze the relationship between the feature "n_r_ecg_p_01" and the target variable "chronic heart failure".

To do this analysis, we would need access to the dataset containing the myocardial infarction complications data, specifically the values for the feature "n_r_ecg_p_01" and the corresponding information on whether or not chronic heart failure is present.

Since we don't have access to the data, we cannot conduct a specific analysis. However, based on prior knowledge, we can assume the following relationship:

If the feature "n_r_ecg_p_01" has the value 'yes', it is more likely that chronic heart failure is present as premature atrial contractions on an ECG can be an indication of heart rhythm abnormalities, including chronic heart failure.

If the feature "n_r_ecg_p_01" has the value 'no', it is less likely that chronic heart failure is present, as the absence of premature atrial contractions on an ECG suggests a normal heart rhythm.

Based on this analysis, we can create a dictionary with the possible values of the feature "n_r_ecg_p_01" for each target class as follows:

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

Note: Since the values for the feature "n_r_ecg_p_01" are categorical with fixed categories of 'no' and 'yes', there is no need to include other possible values in the dictionary.