Based on my prior knowledge, the RestingECG feature is relevant to the task of determining whether the patient shows a heart disease. The RestingECG results can provide information about the electrical activity of the heart and can be indicative of certain heart conditions.

To analyze the relationship between the RestingECG feature and the target variable, we can examine the distribution of RestingECG values for both "yes" (heart disease present) and "no" (heart disease not present) cases.

Let's assume we have a dataset with the following values for the RestingECG feature and corresponding target labels:

RestingECG: ['Normal', 'ST', 'LVH', 'Normal', 'ST', 'ST', 'LVH', 'Normal']
Target: ['no', 'yes', 'no', 'no', 'yes', 'no', 'yes', 'no']

From this sample, we can observe the following:

For "no" (heart disease not present):
- RestingECG values: ['Normal', 'Normal', 'Normal']
- The possible values of RestingECG for target class no are: ['Normal']

For "yes" (heart disease present):
- RestingECG values: ['ST', 'LVH', 'ST', 'LVH']
- The possible values of RestingECG for target class yes are: ['ST', 'LVH']

Based on this analysis, the dictionary would look as follows:

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

Please note that this analysis is based on a small sample and more comprehensive data would be needed to provide a definitive analysis. It is also important to consider other features and conduct further exploratory data analysis to obtain more accurate results.