Based on the feature description, we can analyze the relationship between the "ant_im" feature and the presence of chronic heart failure in the myocardial infarction complications data.

Since "ant_im" refers to the presence or absence of an anterior myocardial infarction, it is reasonable to assume that this feature can be related to chronic heart failure. A myocardial infarction can lead to heart muscle damage, which in turn can increase the risk of developing heart failure.

In order to create the dictionary, we need to determine the possible values of the "ant_im" feature for each target class (yes or no). Since we don't have information about the target class for each patient, we can only make an assumption based on the relationship between the feature and target. 

Assuming that patients with an anterior myocardial infarction are more likely to develop chronic heart failure, and patients without an anterior myocardial infarction are less likely to develop chronic heart failure, we can create the dictionary as follows:

```json
{
	"no": ["QRS has no changes", "there is no infarct in this location"],
	"yes": ["QRS is like QS-complex", "QRS is like QR-complex", "QRS is like Qr-complex"]
}
```
Note that we are excluding the value "QRS has no changes" from the "yes" list since it suggests the absence of an anterior myocardial infarction. However, this value might appear in the "yes" list if it is difficult to predict the presence of chronic heart failure based solely on the "ant_im" feature.

Keep in mind that this analysis is based on assumptions and prior knowledge. Accurate predictions would require further analysis and possibly more information about the data.