Based on prior knowledge, there is a possibility that the likelihood of chronic heart failure differs between males and females. To analyze the relationship between the feature SEX and the task of determining whether the patient shows chronic heart failure, we can look at the distribution of the SEX feature among the two target classes (yes and no) in the myocardial infarction complications data.

Let's assume we have the following data:

| SEX    | Chronic Heart Failure |
|--------|-----------------------|
| male   | yes                   |
| female | no                    |
| female | yes                   |
| male   | no                    |
| male   | no                    |
| female | no                    |

Based on this data, we can see that for the target = "no" class, the possible values of feature SEX are ['female', 'male']. And for the target = "yes" class, the possible values of feature SEX are ['male', 'female'].

Using this information, we can create the required dictionary in the specified format:

```json
{
	"no": ["female", "male"],
	"yes": ["male", "female"]
}
```

Note: If we have more data, the lists in the dictionary might include additional values depending on the distribution of the SEX feature among the two target classes. But for the given data, these are the only possible values.