Based on prior knowledge, the feature "Sex" may have some relationship with the presence of heart disease. Several studies have shown that there are differences in the prevalence of heart disease between males and females. However, since it is a categorical variable with only two possible categories (M for male, F for female), it may not provide a strong predictive power on its own.

To analyze the relationship between the feature "Sex" and the target variable (presence of heart disease), we can examine the distribution of sexes among the patients with and without heart disease.

Here is a dictionary that summarizes the relationship between the "Sex" feature and the target variable:

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

Based on the available information, both males (M) and females (F) have been represented in both the "no" (no heart disease) and "yes" (heart disease) classes. Therefore, we can conclude that the feature "Sex" is not able to predict the presence of heart disease with certainty.

Note that for cases where the relationship is unclear or hard to predict, it is not necessary to include the respective values in the dictionary. However, in this case, both categories are represented in both target classes, so the lists for each target class are not empty.