Based on prior knowledge, we can analyze the relationship between the feature "ritm_ecg_p_01" and the task of determining whether the myocardial infarction complications data of the patient shows chronic heart failure.

Since the feature "ritm_ecg_p_01" represents the ECG rhythm at the time of admission to the hospital, it is likely that different values of this feature can indicate different conditions of the patient's heart. 

To analyze the relationship, we can look at the distribution of "ritm_ecg_p_01" values for each target class (chronic heart failure present or not present). By examining this distribution, we can get an understanding of how different values of "ritm_ecg_p_01" are associated with the presence or absence of chronic heart failure.

Let's create a dictionary to summarize the analysis:

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

Explanation:

- For the target class "no" (chronic heart failure not present), the possible value of "ritm_ecg_p_01" is "yes".
- For the target class "yes" (chronic heart failure present), the possible values of "ritm_ecg_p_01" are both "yes" and "no".

Note: This analysis assumes that the feature "ritm_ecg_p_01" is relevant to the determination of chronic heart failure and that the given categories accurately represent the possible values in the dataset.