Based on prior knowledge, exercise-induced angina is a symptom that can be associated with heart diseases. Therefore, it is expected that the occurrence of exercise-induced angina will be higher for patients with heart disease compared to those without. We need to analyze the relationship between the feature "ExerciseAngina" and the target variable "heart disease" to confirm this relationship.

To do this, we can consider the following steps:

1. Gather the dataset that contains information about both the feature "ExerciseAngina" and the target variable "heart disease" for a significant number of patients.
2. Separate the dataset into two groups based on the target variable: patients with heart disease and patients without heart disease.
3. For each group, determine the possible values of the feature "ExerciseAngina" and create a list of those values.
4. Generate a dictionary with two keys: "no" and "yes", representing the values of the target variable. For each key, store the corresponding list of possible feature values.

Now, let's create the dictionary based on this analysis:

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

According to the analysis, for patients with no heart disease ("no" target class), the possible values of the feature "ExerciseAngina" are only "N". For patients with heart disease ("yes" target class), the possible values of the feature "ExerciseAngina" are "N" and "Y".

Note: As mentioned in the question, it is not necessary to include feature values that are hard to predict in the dictionary. However, we should ensure that the list of each target class is not empty.