To analyze the relationship between the feature "n_p_ecg_p_03" and the presence of chronic heart failure in myocardial infarction complications data, we can examine the distribution of the feature values for each target class.

To create the dictionary, we need to determine the possible values of the feature "n_p_ecg_p_03" for each target class. Let's proceed with the analysis:

For the target class "no" (indicating no chronic heart failure), the possible values of feature "n_p_ecg_p_03" are ['no', 'yes']. Since this class represents the absence of chronic heart failure, it is likely that the feature value "no" will have higher frequency compared to "yes" for this class.

For the target class "yes" (indicating chronic heart failure), the possible values of feature "n_p_ecg_p_03" are ['no', 'yes']. Since this class represents the presence of chronic heart failure, it is likely that the feature value "yes" will have higher frequency compared to "no" for this class.

Based on this analysis, we can create the dictionary as follows:

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

Note that we include all possible values of the feature for each target class, even if some values may be harder to predict.