Based on prior knowledge, the feature "n_p_ecg_p_10" represents the presence or absence of Complete LBBB on the 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 and the task, we need to consider whether the presence or absence of Complete LBBB on the ECG is associated with chronic heart failure.

If the feature value is "yes", indicating the presence of Complete LBBB on the ECG, it is more likely that the patient shows chronic heart failure. On the other hand, if the feature value is "no", indicating the absence of Complete LBBB on the ECG, it is less likely that the patient shows chronic heart failure.

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

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

The list of feature values for the target class "no" is ["no"], indicating that when the feature value is "no" (absence of Complete LBBB on the ECG), the patient is less likely to show chronic heart failure.

Similarly, the list of feature values for the target class "yes" is ["yes"], indicating that when the feature value is "yes" (presence of Complete LBBB on the ECG), the patient is more likely to show chronic heart failure.