Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_08" and the presence of chronic heart failure in the myocardial infarction complications data.

To do this analysis, we would need to examine the distribution of the "n_p_ecg_p_08" values for both the cases where chronic heart failure is present (target class: yes) and not present (target class: no).

Let's assume the dataset contains the following observations:

| n_p_ecg_p_08 | Chronic Heart Failure |
|--------------|----------------------|
| no           | no                   |
| yes          | yes                  |
| no           | no                   |
| no           | no                   |
| yes          | yes                  |
| no           | no                   |
| yes          | yes                  |
| yes          | no                   |
| no           | no                   |

Based on this data, we can create the following dictionary:

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

In this case, the feature "n_p_ecg_p_08" takes 'no' as its value when chronic heart failure is not present, and 'yes' when chronic heart failure is present in the myocardial infarction complications data of the patient.