Based on prior knowledge, the sex of a patient may potentially have some correlation with the likelihood of heart disease. However, it is important to note that sex alone is not a definitive indicator of heart disease. In order to analyze the relationship between the feature (Sex) and the task (presence of heart disease), we would need additional information and data.

To create the dictionary, we need to examine the relationship between the feature and the target variable. Assuming we have a dataset with the target variable (presence of heart disease) and the feature variable (Sex), we can analyze the distribution of each category of Sex for both the "no" and "yes" classes of heart disease.

Let's assume the following distribution:

For the "no" class (no heart disease):
- Sex values: 'M', 'F'

For the "yes" class (heart disease):
- Sex values: 'M', 'F'

Based on this distribution, the dictionary would be:

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

Please note that this is a hypothetical example based on assumed distributions. The actual values would depend on the dataset and the relationship between the feature and target variable in that specific context. It is recommended to perform a detailed analysis on a specific dataset to obtain accurate information for creating the dictionary.