Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_09" (Incomplete LBBB on ECG at the time of admission to hospital) and the presence of chronic heart failure in the myocardial infarction complications data of the patient.

To analyze this relationship, we can look at the distribution of "n_p_ecg_p_09" values for each target class (yes - chronic heart failure, no - no chronic heart failure). By comparing the distribution of values, we can determine if there is any significant relationship between the feature and the target.

Now, let's create the dictionary with the values:

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

Since the categorical variable "n_p_ecg_p_09" only has two categories ('no' and 'yes'), we include both categories in the dictionary for the respective target classes.