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

From the provided feature description, it seems that "INF_ANAM" represents the quantity of myocardial infarctions in the patient's history. The feature has categorical values, with categories 'one', 'zero', 'two', and 'three and more'.

To analyze the relationship, we need to gather information on the occurrences of chronic heart failure for each value of "INF_ANAM". Specifically, we need to see how many cases of chronic heart failure are present for each category.

Here is an example analysis based on hypothetical data:

- "INF_ANAM" category 'one':
  - Chronic Heart Failure (YES): 10 cases
  - Chronic Heart Failure (NO): 15 cases

- "INF_ANAM" category 'zero':
  - Chronic Heart Failure (YES): 5 cases
  - Chronic Heart Failure (NO): 30 cases

- "INF_ANAM" category 'two':
  - Chronic Heart Failure (YES): 20 cases
  - Chronic Heart Failure (NO): 25 cases

- "INF_ANAM" category 'three and more':
  - Chronic Heart Failure (YES): 30 cases
  - Chronic Heart Failure (NO): 5 cases

Based on this analysis, we can create the dictionary:

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

In this example, the category 'zero' of "INF_ANAM" does not have any cases of chronic heart failure. Hence, it is not necessary to include it in the "no" list.

Please note that the actual analysis will depend on the dataset and available data, which was not provided.