Based on prior knowledge, the feature "Sex" may have some association with the presence or absence of heart disease. It is known that heart disease can affect both males and females, but certain types of heart disease may be more prevalent in one sex compared to the other. Therefore, it is reasonable to assume that analyzing the relationship between the "Sex" feature and the target variable "heart disease" may provide some insights.

To create the dictionary, we need to analyze the relationship between the "Sex" feature and the target variable "heart disease". Let's assume that the target variable "heart disease" has two classes: "yes" indicating the presence of heart disease and "no" indicating the absence of heart disease.

Here is a dictionary with the possible values of the "Sex" feature for each target class:

```json
{
    "no": ["M", "F"],
    "yes": ["M", "F"]
}
```

In this case, both target classes have the same possible values for the "Sex" feature, which are "M" for males and "F" for females. This implies that the "Sex" feature is not revealing any specific information regarding the presence or absence of heart disease in this dataset.