Based on the given feature description, it seems that the feature "n_r_ecg_p_04" represents the presence or absence of frequent premature ventricular contractions on ECG at the time of admission to the hospital. The target variable is whether the patient shows chronic heart failure or not.

To analyze the relationship between the feature "n_r_ecg_p_04" and the target variable (chronic heart failure), we would need access to the data to perform a statistical analysis. However, based on prior knowledge, we can make some general assumptions:

1. If the value of "n_r_ecg_p_04" is "yes", it indicates the presence of frequent premature ventricular contractions on ECG.
2. If the value of "n_r_ecg_p_04" is "no", it indicates the absence of frequent premature ventricular contractions on ECG.

To generate the requested dictionary, we can assume the following possibilities:

- If the patient shows chronic heart failure (target class "yes"), the possible values of "n_r_ecg_p_04" could be both "yes" and "no".

- If the patient does not show chronic heart failure (target class "no"), the possible values of "n_r_ecg_p_04" could also be both "yes" and "no".

So, based on these assumptions, the dictionary can be structured as follows:

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

Please note that without specific data, it is challenging to accurately determine the relationship between the feature and the target variable. The provided dictionary assumes that the feature can have both values for both target classes, but this is subject to change depending on the actual data analysis.