Based on my prior knowledge, I can analyze the relationship between the feature "ANT_CA_S_n" and the target variable "chronic heart failure" in the myocardial infarction complications data.

To determine whether the patient shows chronic heart failure, we need to analyze how the use of calcium channel blockers (ANT_CA_S_n) in the ICU relates to the presence or absence of chronic heart failure.

To conduct this analysis, we can examine the distribution of the "ANT_CA_S_n" values for each class of the target variable. By comparing the distribution of the feature values between the classes of the target variable, we can determine if there is a relationship.

Here's an example dictionary showcasing the potential format of the analysis results:

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

In this example, the feature "ANT_CA_S_n" has two possible values: "yes" and "no". Among the patients without chronic heart failure (target class "no"), both "yes" and "no" values are present. Similarly, among the patients with chronic heart failure (target class "yes"), both "yes" and "no" values are present.

Please note that this example dictionary is for illustrative purposes, and the actual values and distribution may differ depending on the specific dataset being analyzed. It is important to thoroughly analyze the relationship between the feature and target using the dataset at hand to generate an accurate dictionary representing the relationship.