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

To do this, we need to gather data on patients with myocardial infarction complications and check if they have chronic heart failure.

Let's assume we have a dataset with the following information:

| IM_PG_P  | Chronic Heart Failure |
|----------|-----------------------|
| no       | no                    |
| yes      | yes                   |
| yes      | yes                   |
| no       | yes                   |
| no       | no                    |
| yes      | no                    |

From the table, we can see that out of the patients who have a right ventricular myocardial infarction (IM_PG_P), some show chronic heart failure and some do not.

Now, let's create the dictionary based on the analysis:

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

In the dictionary, the target class "no" has the corresponding IM_PG_P value of "no" and target class "yes" has the corresponding IM_PG_P values of both "yes" and "no".

Please note that for this specific analysis, we have assumed that the presence of a right ventricular myocardial infarction (IM_PG_P) feature can have the values "yes" or "no" only.