Based on prior knowledge, we can analyze the relationship between the feature "INF_ANAM" and the task of determining whether the patient shows chronic heart failure.

Since "INF_ANAM" represents the quantity of myocardial infarctions in the patient's medical history, it is likely that patients with a higher quantity of myocardial infarctions are more prone to developing chronic heart failure. Therefore, patients with "two" or "three and more" myocardial infarctions in the anamnesis are more likely to show chronic heart failure compared to patients with "zero" or "one" myocardial infarction.

Based on this analysis, we can create the following dictionary:

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

This dictionary indicates that patients with "one" or "zero" myocardial infarctions in the anamnesis are less likely to show chronic heart failure, while patients with "two" or "three and more" myocardial infarctions are more likely to show chronic heart failure.

Note: If there are other categories in the feature "INF_ANAM" that are not mentioned in the analysis, we do not include them in the dictionary as they are hard to predict. However, we ensure that each list of values for the target classes is not empty.