Based on the given feature description and task, we need to analyze the relationship between the feature "IBS_POST" and the presence of chronic heart failure.

First, we need to determine how the different categories of "IBS_POST" relate to the presence or absence of chronic heart failure. This can be done by comparing the frequency or proportion of each category within the "IBS_POST" feature for the two target classes: "yes" (indicating chronic heart failure) and "no" (indicating no chronic heart failure).

Let's assume that we have a dataset with the target variable and the feature "IBS_POST". We can then calculate the proportions or frequencies of each category of "IBS_POST" for the two target classes:

For the "no" class (no chronic heart failure):
- IBS_POST categories with a higher frequency:
    - 'there was no СHD'
- IBS_POST categories with a lower frequency:
    - 'exertional angina pectoris'
    - 'unstable angina pectoris'

For the "yes" class (chronic heart failure):
- IBS_POST categories with a higher frequency:
    - 'unstable angina pectoris'
- IBS_POST categories with a lower frequency:
    - 'there was no СHD'
    - 'exertional angina pectoris'

Based on this analysis, we can create the following dictionary:

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

Please note that the values in the dictionary are based on hypothetical analysis and may vary depending on the actual data.