Based on prior knowledge, let's analyze the relationship between the Age feature and the presence of heart disease.

Assuming that heart disease is more likely to occur in older individuals, we can hypothesize that higher values of Age may be associated with a higher likelihood of heart disease. However, it is essential to consider that heart disease can also affect younger individuals, so the relationship might not be solely dependent on age.

To create the dictionary, we need to gather information about the Age feature for both the "no" and "yes" target classes. Here's an analysis with possible values for each target class:

- Target class "no" (indicating no heart disease):

Typical age values for individuals without heart disease could be:

1. 35.6
2. 43.2
3. 52.1
4. 38.9
5. 49.7

- Target class "yes" (indicating presence of heart disease):

Typical age values for individuals with heart disease could be:

1. 56.3
2. 64.8
3. 60.2
4. 52.6
5. 58.9

Based on this analysis, the generated dictionary in markdown format would be:

```json
{
	"no": [35.6, 43.2, 52.1, 38.9, 49.7],
	"yes": [56.3, 64.8, 60.2, 52.6, 58.9]
}
```