Based on prior knowledge, the ST_Slope feature is a categorical variable that represents the slope of the peak exercise ST segment. The categories for this variable are 'Up', 'Flat', and 'Down'. 

To analyze the relationship between the ST_Slope feature and the presence of heart disease, we can examine the distribution of the ST_Slope values for each target class (i.e., 'yes' and 'no'). This will help us determine if there is any significant association between the feature and the target.

Let's assume we have a dataset with the ST_Slope feature and the corresponding target variable for coronary angiography results. We can group the data by the target variable and calculate the distribution of the ST_Slope values for each group. Based on this analysis, we can generate the corresponding dictionary.

Example Analysis:

For target class 'yes' (presence of heart disease):
- ST_Slope values: ['Up', 'Flat', 'Down']

For target class 'no' (absence of heart disease):
- ST_Slope values: ['Up', 'Flat', 'Down']

Based on this analysis, the dictionary would be:

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

Please note that this analysis assumes that the ST_Slope feature is independent of the target variable. To confirm this, further statistical tests or modeling techniques may be required.

