Based on prior knowledge, the presence of an anterior myocardial infarction (ant_im) can be indicative of chronic heart failure (CHF) in some cases. To analyze the relationship between the feature and the task, we need to examine the possible values of the feature and their association with the target variable.

Considering the categorical feature "ant_im" and the task of determining if the patient shows chronic heart failure, we can analyze the relationship as follows:

- If the feature value is "QRS is like QS-complex", it is likely that the patient does not have chronic heart failure.
- If the feature value is "QRS has no changes", it is unlikely that the patient has chronic heart failure.
- If the feature value is "there is no infarct in this location", it is unlikely that the patient has chronic heart failure.
- If the feature value is "QRS is like QR-complex", it is likely that the patient has chronic heart failure.
- If the feature value is "QRS is like Qr-complex", it is likely that the patient has chronic heart failure.

Based on this analysis, we can create a dictionary with the following information:

```json
{
	"no": ["QRS is like QS-complex", "QRS has no changes", "there is no infarct in this location"],
	"yes": ["QRS is like QR-complex", "QRS is like Qr-complex"]
}
```

Note that we only included the feature values that have a clear association with the target variable. Other values are not included in the dictionary to avoid clutter, as they are hard to predict and may not contribute significantly to the determination of chronic heart failure.