Based on prior knowledge, the feature fibr_ter_03 represents whether the patient received fibrinolytic therapy by Celiasum 3m IU. In order to analyze the relationship between this feature and the presence of chronic heart failure in myocardial infarction complications, we can examine the distribution of fibr_ter_03 values for each target class.

To perform this analysis, we need access to a dataset containing the feature fibr_ter_03 and the target variable indicating the presence or absence of chronic heart failure. Once we have the dataset, we can calculate the frequency of each fibr_ter_03 value within each target class.

Let's assume we have access to such a dataset. Here's an example of how the dictionary could be filled:

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

In this example, the target class "no" contains both "no" and "yes" values for fibr_ter_03, indicating that some patients who did not receive fibrinolytic therapy by Celiasum 3m IU still do not have chronic heart failure. On the other hand, the target class "yes" only contains the "yes" value for fibr_ter_03, suggesting that patients who received this therapy are more likely to have chronic heart failure.