Based on prior knowledge, we can analyze the relationship between the feature "ANT_CA_S_n" and the task of determining if the patient shows chronic heart failure.

Since the feature represents the use of calcium channel blockers in the ICU, it is reasonable to assume that patients who were administered calcium channel blockers may have a lower likelihood of developing chronic heart failure. On the other hand, patients who were not administered calcium channel blockers may have a higher likelihood of developing chronic heart failure.

To create the dictionary, we need to identify the possible values of the feature "ANT_CA_S_n" for each target class (yes and no).

Let's assume we have the following distribution of values:

For target class "no":
- ANT_CA_S_n values that indicate chronic heart failure: ['yes']
- ANT_CA_S_n values that do not indicate chronic heart failure: ['no']

For target class "yes":
- ANT_CA_S_n values that indicate chronic heart failure: ['yes']
- ANT_CA_S_n values that do not indicate chronic heart failure: ['no']

Based on these assumptions, we can create the dictionary as follows:

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

Please note that this is a hypothetical scenario and actual data analysis would require examining the dataset and conducting proper statistical analysis to determine the relationship between the feature and the target variable.