Based on prior knowledge, the ST_Slope feature is related to the task of determining whether a patient's coronary angiography shows a heart disease. The ST segment represents the electrical activity of the heart during exercise, and its slope can provide insights into potential heart diseases.

To analyze the relationship between the ST_Slope feature and the presence of heart disease, we need to examine the distribution of each category of ST_Slope among patients with and without heart disease.

Let's assume we have a dataset with information on patients' coronary angiography results, including the ST_Slope feature and whether they have heart disease or not. We can then perform exploratory data analysis on this dataset to gain insights.

Here is an example dictionary with the format requested, based on the analysis:

```json
{
	"no": ["Flat", "Down"], 
	"yes": ["Up", "Flat", "Down"]
}
```

This dictionary indicates the possible values of the ST_Slope feature for each target class. Patients without heart disease (target class "no") tend to have "Flat" or "Down" ST_Slope values. On the other hand, patients with heart disease (target class "yes") show a wider range of ST_Slope values, including "Up", "Flat", and "Down".

It's important to note that this example dictionary is based on hypothetical data and the relationship between ST_Slope and heart disease may vary in different datasets. A more accurate analysis would require examining actual data and considering other relevant features for a comprehensive understanding of the relationship.