Based on prior knowledge, we can analyze the relationship between the feature SVT_POST and the target variable ("Does the myocardial infarction complications data of this patient show chronic heart failure?") to determine if there is any correlation.

To accomplish this, we should assess the distribution of SVT_POST values for each target class ("yes" and "no") and analyze if there are any noticeable patterns or differences between the two.

Once we have completed the analysis, we can create a dictionary that includes the possible values of feature SVT_POST for each target class. However, it is worth noting that if there are specific SVT_POST values that are challenging to predict, we do not need to include them in the dictionary. Nevertheless, we need to make sure that the list of each target class is not empty.

Here is an example of a dictionary in Markdown format, which details the possible values of feature SVT_POST for each target class:

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

In this example, the target class "no" has two possible values for feature SVT_POST: "no" and "yes". On the other hand, the target class "yes" only has one possible value, which is "yes".