Based on prior knowledge, the BMI feature can provide insight into the likelihood of a person having diabetes. 

Typically, a high BMI is associated with an increased risk of developing diabetes. However, it is important to note that BMI alone is not a definitive indicator, as other factors such as genetics and lifestyle choices also play a role. 

Considering this, let's assume the following ranges for BMI values and examine the relationship between BMI and the target variable:

- Underweight: BMI < 18.5
- Normal weight: 18.5 <= BMI < 25
- Overweight: 25 <= BMI < 30
- Obese class I: 30 <= BMI < 35
- Obese class II: 35 <= BMI < 40
- Obese class III: BMI >= 40

Based on these ranges, let's create a dictionary to represent the relationship between BMI and the target variable:

```json
{
	"yes": [30.5, 32.7, 35.8, 37.1, 40.2],  
	"no": [23.6, 24.9, 26.3, 28.4, 29.7]
}
```

In the dictionary above, the "yes" key represents individuals with diabetes, and the "no" key represents individuals without diabetes. The values associated with each key are representative BMI values for each target class.

Please note that these values are just samples and may not represent the entire range of possible BMI values for each target class.