Based on prior knowledge, Glucose levels can be an important indicator of diabetes. The relationship between the Glucose feature and the task of determining whether a person has diabetes can be analyzed by looking at the distribution of Glucose levels for each target class.

To generate the dictionary, I will first need a dataset containing labeled samples indicating whether a person has diabetes or not, as well as the corresponding Glucose values.

Here is an example dictionary based on a hypothetical dataset:

```json
{
	"yes": [193.0, 162.0, 178.0, 197.0, 216.0],
	"no": [105.0, 90.0, 112.0, 95.0, 100.0]
}
```

In this example, the "yes" class represents people with diabetes, and the "no" class represents people without diabetes. The Glucose values provided are just hypothetical values and can vary depending on the actual dataset.

Please note that without a specific dataset, the values provided here are just for demonstration purposes and may not be accurate or representative of real data.