Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_04" and the task of determining whether the patient shows chronic heart failure.

To analyze this relationship, we can compare the distribution of "n_p_ecg_p_04" for patients with and without chronic heart failure.

Here is a possible analysis:

- For patients with chronic heart failure (target class "yes"), we might expect to see a higher prevalence of Type 1 Second-degree AV block (Mobitz I/Wenckebach) on ECG at the time of admission to the hospital ("n_p_ecg_p_04" = "yes").
- For patients without chronic heart failure (target class "no"), we might expect to see a lower prevalence of Type 1 Second-degree AV block (Mobitz I/Wenckebach) on ECG at the time of admission to the hospital ("n_p_ecg_p_04" = "no").

Based on this analysis, we can create the following dictionary:

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

In this case, the possible values of "n_p_ecg_p_04" for the target class "no" is ["no"], indicating that patients without chronic heart failure generally have "n_p_ecg_p_04" equal to "no". The possible values of "n_p_ecg_p_04" for the target class "yes" are ["no", "yes"], suggesting that patients with chronic heart failure can have both "no" and "yes" values for "n_p_ecg_p_04".