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

To analyze this relationship, we would need to examine a dataset that includes both the "INF_ANAM" feature and information on whether the patient shows chronic heart failure. By comparing the "INF_ANAM" values for patients with and without chronic heart failure, we can identify any patterns or trends that may indicate a relationship.

Once the analysis is completed, we can generate a dictionary to summarize the relationship between the feature and the target variable. Here is an example of how the dictionary may look based on the analysis:

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

In this example, the target class "no" corresponds to patients without chronic heart failure, and the feature values "zero" and "two" are associated with this target class. The target class "yes" corresponds to patients with chronic heart failure, and the feature values "one" and "three and more" are associated with this target class.

Please note that this is just an example, and the actual relationship between the feature and the target variable would depend on the specific dataset and analysis.