Based on prior knowledge, we need to analyze the relationship between the feature "n_r_ecg_p_02" and the target variable "chronic heart failure" in order to determine if the patient has chronic heart failure or not.

To do this, we need to examine the distribution of "n_r_ecg_p_02" values for each category of chronic heart failure and determine if there is any association between the two variables.

Here's the analysis:

- If the feature value for "n_r_ecg_p_02" is "no" and the patient does not have chronic heart failure, we can include this value in the dictionary as a possible value for the target class "no".

- If the feature value for "n_r_ecg_p_02" is "no" and the patient does have chronic heart failure, we can include this value in the dictionary as a possible value for the target class "yes".

- If the feature value for "n_r_ecg_p_02" is "yes" and the patient does not have chronic heart failure, we can include this value in the dictionary as a possible value for the target class "no".

- If the feature value for "n_r_ecg_p_02" is "yes" and the patient does have chronic heart failure, we can include this value in the dictionary as a possible value for the target class "yes".

Based on this analysis, here's the dictionary:

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