Based on prior knowledge, there could be a relationship between gender and chronic heart failure. However, gender alone may not be sufficient to accurately predict chronic heart failure in patients with myocardial infarction complications. To analyze the relationship between gender and chronic heart failure, we would need to look at a dataset specifically designed for this task.

Assuming we have a dataset with information on gender (SEX) and chronic heart failure (target variable), we can analyze the relationship between these two variables.

Based on the given task, the dictionary format should provide the possible values of the feature SEX for each target class (yes and no) for chronic heart failure. Here's an example of a dictionary based on the analysis:

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

Since gender is a categorical variable with only two possible values ("male" and "female"), we include both values for both target classes. However, it is important to note that the actual values in the dictionary will depend on the distribution of the gender feature in the dataset and its relationship with chronic heart failure. The dictionary provided above assumes that both genders (male and female) are present in both target classes.