Based on the given feature "ANT_CA_S_n" and the task of determining whether the patient shows chronic heart failure, we need to analyze the relationship between the feature and the target variable.

To do this analysis, we would ideally have access to a dataset that includes values for the feature "ANT_CA_S_n" and the corresponding information about whether the patient has chronic heart failure or not. With this data, we could examine the distribution of different values of the feature for each target class and determine how they correlate.

However, without access to the actual data, we can make some assumptions based on prior knowledge. Calcium channel blockers (CCBs) are often used in the treatment of hypertension, arrhythmias, and angina. While not a direct treatment for chronic heart failure, CCBs may be prescribed to help manage symptoms and improve heart function. Therefore, it is possible that patients with chronic heart failure might be more likely to have been prescribed CCBs in the ICU.

To create the requested dictionary, we need information about the possible values for the feature "ANT_CA_S_n" and their relationship to the target class of chronic heart failure. Without the actual data, it is hard to predict the exact values that may be present for each target class. However, we can provide a general approach for creating the dictionary.

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

Since the feature is categorical with two possible categories - "yes" and "no" - we can assume that both categories are present in the dataset. Therefore, in the absence of actual data, we can include both possible values in the dictionary for each target class.

Please note that this is a general analysis based on the given information. Without the actual data, it is not possible to provide a more accurate analysis or make specific predictions about the relationship between the feature and the target class.