Based on prior knowledge, we can perform an analysis to determine how the feature "INF_ANAM" relates to the task of detecting chronic heart failure in a patient with myocardial infarction complications.

To analyze this relationship, we will examine the distribution of "INF_ANAM" values for each target class (yes or no) and identify the possible values of "INF_ANAM" for each class.

Let's perform the analysis:

- For the target class "yes" (indicating the presence of chronic heart failure), we examine the occurrences of myocardial infarctions in the anamnesis. We observe the following possible values of "INF_ANAM" associated with chronic heart failure: ['one', 'two', 'three and more'].

- For the target class "no" (indicating the absence of chronic heart failure), the possible value of "INF_ANAM" associated with no chronic heart failure is 'zero'.

Now, let's generate the dictionary with the analysis results:

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

Please note that the list for the target class "no" is not empty, as it contains the value 'zero' which indicates the absence of chronic heart failure.