Based on my prior knowledge, I can analyze the relationship between the feature "inf_im" and the presence of chronic heart failure in patients with myocardial infarction complications. 

Since the feature "inf_im" represents the presence of an inferior myocardial infarction and ECG changes in specific leads, it is likely that certain categories of "inf_im" may be associated with chronic heart failure.

To analyze this relationship, we can examine the frequency of each category of "inf_im" for patients with and without chronic heart failure. By comparing the distribution of these categories, we can determine if any specific values of "inf_im" are more prevalent in patients with chronic heart failure.

Let's create the dictionary with the format requested:

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

In this case, we are including the categories of "inf_im" that are more likely to be associated with chronic heart failure ("QRS is like QS-complex") in the "yes" list. The other categories ("there is no infarct in this location", "QRS is like Qr-complex", "QRS is like QR-complex") are included in the "no" list as they are more likely to be associated with the absence of chronic heart failure.

Please note that if there are additional categories of "inf_im" that are hard to predict or have an unclear relationship with chronic heart failure, we do not need to include them in the dictionary. The focus is on capturing the more meaningful categories that can help determine the presence or absence of chronic heart failure.