Based on my prior knowledge, I cannot make predictions about the relationship between the feature INF_ANAM and the occurrence of chronic heart failure. However, I can analyze the available data to determine the possible values of INF_ANAM for each target class.

To do this, I will look at the myocardial infarction complications data for patients with chronic heart failure and those without it.

Given the categorical variable INF_ANAM with categories ['one', 'zero', 'two', 'three and more'], I will analyze the distribution of these categories in relation to the presence or absence of chronic heart failure.

After this analysis, I will generate a dictionary with the possible values of INF_ANAM for each target class.

Here is the generated dictionary:

```json
{
    "no": ["one", "zero", "two", "three and more"],
    "yes": ["one", "zero", "two", "three and more"]
}
```

Note that the dictionary includes all possible values of INF_ANAM for both target classes, as we don't have any prior knowledge about which values are more or less likely for each class.