Based on prior knowledge, we can analyze the relationship between the feature "INF_ANAM" and the task of determining if the myocardial infarction complications data of a patient show chronic heart failure.

To do this, we need labeled data that maps the feature "INF_ANAM" to the target variable "chronic heart failure." Once we have this data, we can analyze how the different values of "INF_ANAM" are associated with the presence or absence of chronic heart failure.

Let's assume we have the following labeled data:

| INF_ANAM     | Chronic Heart Failure |
|--------------|-----------------------|
| one          | No                    |
| zero         | Yes                   |
| two          | Yes                   |
| three and more | Yes                 |
| zero         | No                    |
| one          | No                    |
| two          | No                    |

From this data, we can observe the association between the feature and the target:

- If the value of "INF_ANAM" is "one," the chronic heart failure is likely to be "No."
- If the value of "INF_ANAM" is "zero," "two," or "three and more," the chronic heart failure is likely to be "Yes."

Based on this analysis, we can generate the dictionary:

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

Please note that the values "two" and "three and more" are considered associated with chronic heart failure because they have occurred in cases where chronic heart failure is present.