Based on prior knowledge, let's analyze the relationship between the feature "ANT_CA_S_n" (Use of calcium channel blockers in the ICU) and the task of determining if the patient has chronic heart failure.

To analyze this relationship, we can look at previous studies or medical literature to understand how the use of calcium channel blockers might be related to chronic heart failure. Additionally, we can examine any existing data or medical records that provide insights into the relationship.

Once we have gathered this information, we can create a dictionary that lists the possible values of the feature "ANT_CA_S_n" for each target class ("yes" for chronic heart failure and "no" for no chronic heart failure).

Here is an example of how the dictionary might look:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```

In this example, the feature "ANT_CA_S_n" has only one possible value for each target class. However, if there are multiple possible values, we would include them in the respective lists. It is not necessary to include values that are hard to predict, as long as each list of values for each target class is not empty.