To analyze the relationship between the feature "IBS_POST" and the target variable "chronic heart failure", we need to examine the distribution of the different values of "IBS_POST" for each target class.

Based on prior knowledge, we can assume that certain categories of "IBS_POST" might be more prevalent in patients with chronic heart failure compared to those without chronic heart failure. Let's explore the possible values of "IBS_POST" for each target class:

For patients with no chronic heart failure (target class "no"):
- IBS_POST: 'there was no СHD'

For patients with chronic heart failure (target class "yes"):
- IBS_POST: 'unstable angina pectoris'

Please note that the possible values for "IBS_POST" mentioned above are based on assumptions and prior knowledge. It's crucial to have a larger dataset and perform statistical analysis to validate these assumptions. However, for the purpose of creating the dictionary, we will consider these possible values.

Based on the above analysis, we can create the dictionary as follows:

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

The dictionary shows the possible values of "IBS_POST" for each target class.