Based on prior knowledge and assuming that chronic heart failure is more likely to occur in older individuals, we can analyze the relationship between age (AGE feature) and the occurrence of chronic heart failure (target variable).

To analyze this, we need data on myocardial infarction complications, including AGE and the presence or absence of chronic heart failure. With this data, we can evaluate the distribution of ages for patients with and without chronic heart failure.

Assuming that the data is available, we will create a dictionary with lists of typical AGE values for the "no" and "yes" classes of the target variable:

```json
{
    "no": [50.5, 41.2, 36.7, 48.9, 55.3],
    "yes": [65.8, 68.4, 72.1, 61.9, 67.2]
}
```

Please note that the values provided in the example are arbitrary and should be replaced with actual data analysis results. The values should represent typical ages found in each class, giving an idea of the relationship between AGE and the presence or absence of chronic heart failure.