Based on prior knowledge, it is expected that the presence of Complete LBBB on ECG at the time of admission to the hospital (n_p_ecg_p_10 feature) may indicate a higher likelihood of chronic heart failure in myocardial infarction patients.

Now, let's conduct a thorough analysis of the relationship between the feature and the task by examining the distribution of the feature values for each target class:

For patients without chronic heart failure (target class: no), the possible values of n_p_ecg_p_10 may include:
- no

For patients with chronic heart failure (target class: yes), the possible values of n_p_ecg_p_10 may include:
- no
- yes

Based on this analysis, let's create the dictionary:

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

Please note that this analysis assumes a general relationship between the feature and the target variable. However, to obtain a more accurate assessment, it is recommended to consult specific domain knowledge or perform further statistical analysis on the dataset.