Based on prior knowledge, we can analyze the relationship between the feature "IM_PG_P" and the task of determining whether the myocardial infarction complications data of the patient shows chronic heart failure.

In this case, the feature "IM_PG_P" refers to the presence of a right ventricular myocardial infarction, and it is a categorical variable with two possible categories: "no" and "yes".

To analyze the relationship, we need to look at the distribution of each category of the feature within the target classes of chronic heart failure. Let's assume the target variable is named "CHF" (short for chronic heart failure).

Here is the analysis and the corresponding dictionary:

1. For the target class "no":
   - "IM_PG_P" values that indicate the absence of right ventricular myocardial infarction ("no") could be relevant in this case. So, we include these values in the dictionary.
   - Values indicating the presence of right ventricular myocardial infarction ("yes") may or may not be relevant to the target class "no" (chronic heart failure), but since we are not specifically looking for this relationship, we will not include these values in the dictionary.

2. For the target class "yes":
   - Values indicating the absence of right ventricular myocardial infarction ("no") may or may not be relevant to the target class "yes" (chronic heart failure), but since we are not specifically looking for this relationship, we will not include these values in the dictionary.
   - "IM_PG_P" values that indicate the presence of right ventricular myocardial infarction ("yes") could be relevant in this case. So, we include these values in the dictionary.

Based on this analysis, the dictionary would be as follows:

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

Please note that as stated in the task requirements, if there are any other values that are hard to predict or are not specifically relevant to determining chronic heart failure, they should not be included in the dictionary, but each list for the target class should not be empty.