Based on my prior knowledge, the presence of a posterior myocardial infarction (post_im) can be an indicator of chronic heart failure.

To analyze the relationship between the feature post_im and the presence of chronic heart failure, we need to examine the distribution of post_im values for each target class.

Here is the analysis of the relationship between the feature post_im and the target variable "chronic heart failure":

For patients without chronic heart failure (target class no):
- post_im values: ['there is no infarct in this location', 'QRS has no changes', 'QRS is like Qr-complex']
- We can see that there is no clear indication of post_im values for patients without chronic heart failure.

For patients with chronic heart failure (target class yes):
- post_im values: ['QRS has no changes', 'QRS is like Qr-complex', 'QRS is like QR-complex']
- We can observe that patients with chronic heart failure have a subset of the post_im values seen for patients without chronic heart failure.

Based on this analysis, we can create the dictionary:

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

Please note that we only include the post_im values that show a clear distinction between the two target classes.