Based on the given feature "IBS_POST" and the task of determining whether the patient has chronic heart failure, we will analyze the relationship between the feature and the target variable.

From prior knowledge, it is expected that the feature "IBS_POST" could be related to chronic heart failure. Different values of "IBS_POST" may correspond to different probabilities of chronic heart failure.

To generate the dictionary with the specific details, we need information about the distribution of "IBS_POST" values for each target class (yes, no). So, we'll analyze the relationship between "IBS_POST" and chronic heart failure:

For the target class "no" (patients without chronic heart failure):
- There was no СHD: This category suggests that there was no coronary heart disease, which may indicate a lower probability of chronic heart failure in these patients.

For the target class "yes" (patients with chronic heart failure):
- Exertional angina pectoris: This category suggests the presence of exertional angina pectoris, which may indicate a higher probability of chronic heart failure in these patients.
- Unstable angina pectoris: This category suggests the presence of unstable angina pectoris, which may also indicate a higher probability of chronic heart failure in these patients.

Based on this analysis, we can create the dictionary with the specific details:

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

Note that we only included the values that are likely to have a relationship with chronic heart failure. Other values of "IBS_POST" are not necessary to include in the dictionary, as long as each target class has at least one value listed.