Based on prior knowledge, the ST_Slope feature is related to the peak exercise ST segment on a coronary angiography. This feature is categorical and has three possible values: 'Up', 'Flat', and 'Down'. 

To analyze the relationship between ST_Slope and the presence of heart disease, we can look at the distribution of each ST_Slope category among patients with and without heart disease.

Let's assume we have a dataset with the following information:

| ST_Slope | Heart Disease |
|----------|---------------|
| Up       | Yes           |
| Flat     | Yes           |
| Flat     | No            |
| Down     | Yes           |
| Flat     | No            |
| Up       | No            |
| Down     | No            |
| Up       | Yes           |
| Down     | Yes           |
| Flat     | No            |

Based on this data, we can compute the following dictionary:

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

In this example, among patients without heart disease (target class 'no'), the only observed ST_Slope category is 'Flat'. On the other hand, among patients with heart disease (target class 'yes'), all three ST_Slope categories are observed, namely 'Up', 'Flat', and 'Down'.

Please note that this is a simplified example for illustrative purposes. In a real analysis, you would use a larger dataset with more samples and potentially perform statistical tests to determine the significance of the relationship between the feature and target variables.