Based on my prior knowledge, the RestingECG feature is related to heart diseases. Let's analyze the possible relationship between RestingECG and the presence of heart disease.

To do this analysis, we need data that includes the RestingECG values and the corresponding presence of heart disease (Yes or No). Once we have this data, we can group the RestingECG values by the target classes (Yes and No) and see the distribution of each RestingECG category within the target classes.

For now, let's assume we have the necessary data for analysis.

RestingECG values are categorical and can be one of the following: 'Normal', 'ST', or 'LVH'.

Based on my analysis, the possible values of RestingECG for the target class 'No' (no heart disease) could be:

- 'Normal': Patients with a normal RestingECG are less likely to have heart disease.
- 'ST': Patients with ST abnormalities in their RestingECG may or may not have heart disease, as the diagnosis could vary.
- 'LVH': Patients with RestingECG indicating left ventricular hypertrophy (LVH) may or may not have heart disease, as LVH can be caused by factors other than heart disease.

The possible values of RestingECG for the target class 'Yes' (heart disease present) could be:

- 'Normal': Some patients with a normal RestingECG may still have heart disease, so this value could be present.
- 'ST': Patients with ST abnormalities in their RestingECG are more likely to have heart disease.
- 'LVH': Patients with RestingECG indicating left ventricular hypertrophy (LVH) may or may not have heart disease, as LVH can be caused by factors other than heart disease.

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

```json
{
	"no": ["Normal", "ST", "LVH"],
	"yes": ["Normal", "ST", "LVH"]
}
```

Please note that this is a general analysis based on prior knowledge, and the actual relationship between RestingECG and heart disease may vary depending on the specific dataset or study.