Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_05" (Paroxysms of atrial fibrillation on ECG at the time of admission to hospital) and the task of determining whether the patient has chronic heart failure.

In order to assess this relationship, we need to evaluate the distribution of the feature values for each target class (yes and no) and analyze if there is any significant difference between them.

Let's analyze the possible values of the feature "n_r_ecg_p_05" for each target class and create a dictionary:

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

Since the feature is a categorical variable with only two possible values (yes and no), we can conclude that if a patient has chronic heart failure, the value of the feature "n_r_ecg_p_05" will be "yes". Conversely, if the patient does not have chronic heart failure, the value will be "no".

Therefore, the dictionary shows that if a patient has chronic heart failure, the feature "n_r_ecg_p_05" will have the value "yes", and if the patient does not have chronic heart failure, the feature value will be "no".