Based on prior knowledge, the use of acetylsalicylic acid (ASP_S_n) could potentially be related to the presence or absence of chronic heart failure in patients with myocardial infarction complications. To analyze this relationship, I would first split the data based on the target variable (presence or absence of chronic heart failure) and then examine the distribution of ASP_S_n values within each class.

Let's analyze the relationship between the feature ASP_S_n and the presence or absence of chronic heart failure:

- For patients with chronic heart failure (target class "yes"), the possible values of ASP_S_n could be ['yes', 'no']. This is because acetylsalicylic acid can be both used (yes) or not used (no) in the treatment of chronic heart failure. To find the specific values of ASP_S_n for target class "yes", we would need to examine the data.

- For patients without chronic heart failure (target class "no"), the possible values of ASP_S_n could also be ['yes', 'no']. However, it is also possible that patients without chronic heart failure may not have any records of ASP_S_n in the dataset. In this case, we would need to verify the data to determine the specific values of ASP_S_n for target class "no".

Based on this analysis, we can create a dictionary with the format requested:

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

Please note that this is a general analysis based on known information. The final dictionary may vary depending on the actual data.