Based on the feature description, the feature "ZSN_A" represents the presence of chronic heart failure in the patient's medical history. The feature is a categorical variable with the following categories: ['there is no chronic heart failure', 'I stage', 'IIА stage', 'IIB stage'].

To determine how the feature relates to the target variable, which is the presence of chronic heart failure complications in the patient's myocardial infarction data, we need to analyze the distribution of "ZSN_A" values for the two target classes, "yes" and "no".

Based on prior knowledge, we can expect that patients with a higher stage of chronic heart failure (IIА stage, IIB stage) are more likely to exhibit chronic heart failure complications in their myocardial infarction data, leading to a "yes" outcome for the task. On the other hand, patients with no chronic heart failure or at an earlier stage (I stage) are less likely to have chronic heart failure complications, resulting in a "no" outcome for the task.

Taking these assumptions into account, we can create the dictionary with the relevant details as follows:

```json
{
	"no": ["there is no chronic heart failure", "I stage"], 
	"yes": ["IIА stage", "IIB stage"]
}
```

This dictionary indicates that patients with the "no" outcome for the task are likely to have the "ZSN_A" values of "there is no chronic heart failure" and "I stage". Patients with the "yes" outcome for the task are likely to have the "ZSN_A" values of "IIА stage" and "IIB stage".

Note that this analysis assumes a general relationship based on prior knowledge, and the actual distribution of "ZSN_A" values for the target classes may vary.