Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_02" and the target variable "chronic heart failure".

The feature "n_r_ecg_p_02" represents the presence or absence of frequent premature atrial contractions on the ECG at the time of admission to the hospital. To determine whether this feature is related to chronic heart failure, we need to examine if patients with frequent premature atrial contractions are more likely to have chronic heart failure.

To analyze this relationship, we can categorize the patients based on the value of the feature "n_r_ecg_p_02" (either "no" or "yes") and observe the presence or absence of chronic heart failure in each category. By comparing the occurrence of chronic heart failure in both categories, we can determine if there is a relationship between the feature and the target.

Based on the analysis, we can generate the following dictionary:

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

In this case, since the feature "n_r_ecg_p_02" is a categorical variable, we only have two possible categories: "no" and "yes". For the target class "no" (patients without chronic heart failure), the possible values of the feature "n_r_ecg_p_02" are ["no"], indicating that patients without frequent premature atrial contractions do not have chronic heart failure. Similarly, for the target class "yes" (patients with chronic heart failure), the possible values of the feature "n_r_ecg_p_02" are ["yes"], indicating that patients with frequent premature atrial contractions do have chronic heart failure.

Please note that in this case, the lists for each target class only contain one value, as there are no other possible values for this feature in relation to chronic heart failure.