Based on prior knowledge, it is expected that the RestingECG feature may have some relationship with the occurrence of heart disease. Let's analyze how the different categories of RestingECG may relate to the presence or absence of heart disease.

To conduct this analysis, we would need a dataset that includes the RestingECG values and the corresponding coronary angiography results for a set of patients. From this dataset, we could directly observe the relationship between RestingECG and the presence of heart disease.

Without access to such a dataset, it is challenging to determine the exact relationship between RestingECG and heart disease. However, we can make some general assumptions based on prior knowledge:

1. "Normal": It is expected that a normal resting electrocardiogram result would indicate a lower likelihood of heart disease. Therefore, it is possible that the "no" class (indicating no heart disease) may have a higher frequency of "Normal" RestingECG values compared to the "yes" class (indicating heart disease). The "Normal" category may have a stronger association with the absence of heart disease.

2. "ST": ST abnormalities in the resting electrocardiogram can be an indicator of myocardial ischemia or other heart conditions. Therefore, it is possible that the "yes" class (indicating heart disease) may have a higher frequency of "ST" RestingECG values compared to the "no" class (indicating no heart disease). The "ST" category may have a stronger association with the presence of heart disease.

3. "LVH" (Left Ventricular Hypertrophy): LVH is associated with increased risk of heart disease, particularly in individuals with hypertension. Therefore, it is possible that the "yes" class (indicating heart disease) may have a higher frequency of "LVH" RestingECG values compared to the "no" class (indicating no heart disease). The "LVH" category may have a stronger association with the presence of heart disease.

Based on these assumptions, we can create a dictionary with the possible values of the RestingECG feature for each target class:

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

Please note that since we don't have a dataset to confirm the relationship, the actual values in the dictionary may vary. If we had a dataset, we could calculate the frequencies of each RestingECG category for each target class to obtain a more accurate representation of the relationship.