Based on the given feature "ant_im" (Presence of an anterior myocardial infarction), we need to analyze its relationship with the presence of chronic heart failure for a specific patient.

As a first step, let's assume we have a dataset that contains information about patients' myocardial infarction complications and whether they show chronic heart failure or not. Using this dataset, we can analyze the relationship between the "ant_im" feature and the target variable.

After conducting the analysis, we can create a dictionary with the specified format, including the possible values of the "ant_im" feature for each target class.

Based on prior knowledge, we can assume that certain values of the "ant_im" feature might be more indicative of chronic heart failure than others. In this case, we can include those values in the dictionary, while excluding the values that are not informative or hard to predict.

Let's suppose that after analyzing the data, we find the following relationship:

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

In this case, the values "QRS has no changes" and "there is no infarct in this location" of the "ant_im" feature are more associated with the absence of chronic heart failure ("no" class). On the other hand, the values "QRS is like QS-complex" and "QRS is like QR-complex" are more associated with the presence of chronic heart failure ("yes" class).

Please note that this specific analysis is based on prior knowledge and assumptions. The actual relationship between the "ant_im" feature and chronic heart failure might vary depending on the dataset and the specific context.