Based on my prior knowledge, the feature np_05, which indicates the presence of LBBB (anterior branch) in the patient's history, can be analyzed to determine if it is related to the occurrence of chronic heart failure.

To analyze the relationship between the feature np_05 and the task of predicting chronic heart failure, we can gather data on patients who have experienced myocardial infarction complications and determine whether they subsequently developed chronic heart failure or not.

Based on this analysis, we can create a dictionary to summarize the relationship between the feature np_05 and the occurrence of chronic heart failure:

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

Since the feature np_05 is a categorical variable with only two possible values ("no" and "yes"), we can determine that if the value of np_05 is "no", the patient does not show chronic heart failure, and if the value is "yes", the patient does show chronic heart failure.

The lists for each target class contain the possible values of feature np_05 that correspond to that class. In this case, there is a one-to-one mapping between the feature values and the target classes, so there is no need to exclude any values from the dictionary.