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

To do this, we need to examine the values of the "IBS_POST" feature for both the cases where chronic heart failure is present (yes) and when it is not present (no).

Let's assume we have the following data:

| IBS_POST                               | Chronic Heart Failure |
|----------------------------------------|-----------------------|
| "there was no СHD"                     | no                    |
| "exertional angina pectoris"           | yes                   |
| "unstable angina pectoris"             | yes                   |
| "there was no СHD"                     | no                    |
| "exertional angina pectoris"           | yes                   |
| "exertional angina pectoris"           | no                    |

Based on this data, we can create a dictionary with the possible values of the "IBS_POST" feature for each target class:

```json
{
	"no": ["there was no СHD", "exertional angina pectoris"],
	"yes": ["exertional angina pectoris", "unstable angina pectoris"]
}
```

In this case, we excluded the value "unstable angina pectoris" for the "no" target class, and the value "there was no СHD" for the "yes" target class, as they were less frequently observed in the data. The important aspect is to ensure that the lists of each target class are not empty.

Please note that this analysis is based on the given data and might vary if additional data is available or if the data distribution is different.