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

To analyze this relationship, we need to examine the possible values of the "fibr_ter_08" feature for each target class ("no" and "yes").

Analyzing the relationship between feature fibr_ter_08 and chronic heart failure:

- If the value of fibr_ter_08 is "no" for all instances with chronic heart failure, then there is a strong negative relationship between fibr_ter_08 and chronic heart failure.
- If the value of fibr_ter_08 is "yes" for all instances with chronic heart failure, then there is a strong positive relationship between fibr_ter_08 and chronic heart failure.
- If there is a mix of "yes" and "no" values for fibr_ter_08 within the instances with chronic heart failure, then the relationship between fibr_ter_08 and chronic heart failure is not straightforward.

Based on this analysis, we can create a dictionary with the possible values of fibr_ter_08 for each target class:

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

In this case, the dictionary indicates that the only possible value of fibr_ter_08 for instances without chronic heart failure ("no" class) is "yes", and the only possible value for instances with chronic heart failure ("yes" class) is "no".