Based on prior knowledge, we can analyze the relationship between the feature ritm_ecg_p_08 and the presence of chronic heart failure in myocardial infarction patients.

For this analysis, we need data that includes the values of ritm_ecg_p_08 for patients with and without chronic heart failure.

Assuming we have the necessary data, we can analyze the relationship between the feature and the task by examining the distribution of ritm_ecg_p_08 values for each target class (yes or no for chronic heart failure).

Let's assume we have the following data:

| ritm_ecg_p_08 | Chronic Heart Failure |
|--------------|----------------------|
| no           | Yes                  |
| yes          | No                   |
| yes          | Yes                  |
| no           | No                   |
| no           | No                   |

Based on this data, we can observe the following:

For chronic heart failure = No:
- Possible values of ritm_ecg_p_08: ['no']

For chronic heart failure = Yes:
- Possible values of ritm_ecg_p_08: ['yes']

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

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

Please note that this analysis is based on a small sample of data and might not represent the true relationship between ritm_ecg_p_08 and chronic heart failure.