Based on prior knowledge, the Age feature can provide useful information in determining the presence or absence of heart disease. From medical research, it is known that the risk of heart disease increases with age. Therefore, it is expected that higher age values would be associated with a higher likelihood of heart disease.

To create the required dictionary, we will assume that the target variable 'yes' indicates the presence of heart disease and 'no' indicates the absence of heart disease. 

Thorough analysis would involve examining the dataset and calculating the average age for each target class. However, since we don't have access to the actual dataset, we will rely on assumptions and create a dictionary with typical age ranges for each target class.

```json
{
	"no": [30.0, 40.0, 50.0, 60.0, 70.0],
	"yes": [55.0, 65.0, 70.0, 75.0, 80.0]
}
```

In this case, we have assumed that the 'no' class represents patients without heart disease and the 'yes' class represents patients with heart disease. The typical age values provided in the dictionary are meant to represent possible values for each class, with lower values representing the 'no' class and higher values representing the 'yes' class.

Please note that this analysis is based on assumptions and prior knowledge. In a real scenario, it would be necessary to have access to the actual dataset and perform a thorough analysis to determine the actual relationship between the Age feature and the presence of heart disease.