Based on prior knowledge, the presence of complete LBBB on ECG at the time of admission to the hospital can indicate chronic heart failure in patients. Therefore, the feature n_p_ecg_p_10 is likely related to the task of determining if the patient's myocardial infarction complications data shows chronic heart failure.

To create the dictionary, we need to analyze how the feature values are distributed among the target variable classes (no and yes).

Here is the resulting dictionary:

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

In this case, the category "no" represents the absence of complete LBBB on ECG at the time of admission to the hospital, while the category "yes" represents the presence of complete LBBB on ECG at the time of admission to the hospital.

Since the categorical variable with categories ['no', 'yes'] includes all possible values, there is no need to include any hard-to-predict values in the dictionary.