Based on prior knowledge, there might be some relationship between gender (SEX) and the development of chronic heart failure (target variable) in patients with myocardial infarction complications. However, gender alone is unlikely to be a strong predictor of chronic heart failure. Nonetheless, we can still analyze the available data and generate a dictionary with details regarding the relationship between gender and the target variable.

To accomplish this, we will need access to a dataset that includes the SEX and chronic heart failure status (yes or no) for each patient. With the dataset, we can examine the distribution of gender among patients with and without chronic heart failure.

Let's assume we have obtained the necessary data and have performed the analysis. Here's an example dictionary with the corresponding details:

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

In this example, the dictionary suggests that both males and females can develop chronic heart failure as a complication of myocardial infarction. The assumption is that there are records for both genders in both the "no" and "yes" target classes.

The dictionary is formatted as a markdown code snippet and provides the possible values of the SEX feature for each target class. Keep in mind that this is just an example based on hypothetical data and analysis. The actual dictionary may differ depending on the data and analysis you perform.