Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_08" and the task of determining whether the myocardial infarction complications data of this patient shows chronic heart failure.

To analyze this relationship, we can look at the distribution of the "fibr_ter_08" feature for each target class. We will check for any patterns or differences in the values of "fibr_ter_08" between the two target classes.

Let's start by examining the possible values of the "fibr_ter_08" feature and their distribution for each target class.

**Target Class: no**
- Possible values of "fibr_ter_08": ['no', 'yes']

**Target Class: yes**
- Possible values of "fibr_ter_08": ['no', 'yes']

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

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

Since there are only two possible values for the "fibr_ter_08" feature ('no' and 'yes'), we can include both values in the dictionary for each target class.