Based on my prior knowledge, I can analyze the relationship between the feature "IBS_POST" and the task of determining whether the patient's myocardial infarction complications data show chronic heart failure.

To do this analysis, I would need a dataset with information about the patients' myocardial infarction complications and whether they have chronic heart failure or not.

Once I have the dataset, I can examine the distribution of the "IBS_POST" feature for both cases - patients with chronic heart failure (target class: yes) and patients without chronic heart failure (target class: no).

Based on the provided feature description, the possible values of "IBS_POST" are:

- "there was no CHD"
- "exertional angina pectoris"
- "unstable angina pectoris"

To analyze the relationship, I would look at the "IBS_POST" values for each target class and see if there are any patterns or notable differences. If certain values of "IBS_POST" are more prevalent in one target class compared to the other, it might indicate a relationship.

Once I have conducted the analysis, I can generate the dictionary as requested. However, since we do not have the actual dataset, I cannot provide the specific values for the target classes. Nonetheless, I can still generate an example dictionary structure:

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

Please note that this example dictionary is hypothetical and the actual values may differ based on the dataset analysis.