Based on the provided feature and task, we need to analyze the relationship between the feature "fibr_ter_01" and the presence of chronic heart failure in the patient.

To conduct this analysis, we can start by examining the distribution of the feature values for both target classes (yes and no) of chronic heart failure. From there, we can determine if there is any clear relationship between the feature and the target.

Here is the analysis dictionary for the feature "fibr_ter_01":

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

Explanation:
- For the target class "no" (not showing chronic heart failure), the possible values of "fibr_ter_01" are ['no'].
- For the target class "yes" (showing chronic heart failure), the possible values of "fibr_ter_01" are ['yes'].

Since the feature "fibr_ter_01" is a categorical variable with the categories 'no' and 'yes', we can see that the presence of chronic heart failure is represented only by the value 'yes' in this feature. Conversely, the absence of chronic heart failure is represented by the value 'no'.

Please note that in this case, there might be other possible values of "fibr_ter_01" that are not mentioned in the dictionary because they are not relevant or informative for predicting chronic heart failure.