Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_02" and the target variable "chronic heart failure."

To determine if the myocardial infarction complications data of this patient shows chronic heart failure, we need to examine the values of the feature "fibr_ter_02" for both cases: "yes" and "no" chronic heart failure.

Based on the information provided, we can create a dictionary with the possible values of the feature "fibr_ter_02" for each target class:

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

The dictionary shows that for the target class "no" (indicating no chronic heart failure), the possible value of the feature "fibr_ter_02" is "no." Similarly, for the target class "yes" (indicating chronic heart failure), the possible value of the feature "fibr_ter_02" is "yes."

Please note that if there are any other possible values for the feature "fibr_ter_02" that are not provided in the dataset, they will not be included in this dictionary.