To analyze the relationship between the feature "ant_im" and the task of determining if the patient shows chronic heart failure, we can look at the possible values of "ant_im" for each target class.

Based on prior knowledge, we can make some assumptions:

1. "ant_im" is related to the presence or absence of an anterior myocardial infarction.
2. It is possible that the presence of an anterior myocardial infarction can indicate chronic heart failure.

Now let's analyze the relationship between "ant_im" and the target variable:

- If the value of "ant_im" is "QRS is like QS-complex" or "QRS is like QR-complex", it suggests that there is an anterior myocardial infarction. This may increase the likelihood of chronic heart failure.
- If the value of "ant_im" is "QRS has no changes" or "there is no infarct in this location", it suggests the absence of an anterior myocardial infarction. This may decrease the likelihood of chronic heart failure.

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

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

Note that we have included the possible values of "ant_im" for each target class. There might be other values for "ant_im" that are not included in the dictionary, as those are hard to predict.