Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_07" and the target variable "chronic heart failure" to determine if fibr_ter_07 is related to chronic heart failure.

To do this, we need to examine the distribution of "fibr_ter_07" values for each class of the target variable, "chronic heart failure."

We can start by analyzing the target variable and identifying the cases where chronic heart failure is present and not present. Then, we can examine the corresponding values of the "fibr_ter_07" feature for these cases.

Once we have analyzed the relationship between "fibr_ter_07" and chronic heart failure, we can create the dictionary with the possible values of "fibr_ter_07" for each target class.

Here is the dictionary with the possible values of the "fibr_ter_07" feature for each target class:

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

Please note that if there are additional values for "fibr_ter_07" that are hard to predict or not included in the dictionary, they can be omitted as requested. However, it is essential to ensure that the lists for each target class are not empty.