Based on prior knowledge, we need to analyze the relationship between the feature "zab_leg_02" (chronic bronchitis in the anamnesis) and the target variable "chronic heart failure". We want to determine if the "zab_leg_02" feature can help predict whether the patient has chronic heart failure or not.

To analyze this relationship, we will look at the values of the "zab_leg_02" feature for each class of the target variable. We will examine which values are associated with chronic heart failure and which values are not.

Let's say we have the following data:

| zab_leg_02 |  chronic heart failure |
|------------|-----------------------|
|    no      |           no          |
|    yes     |          yes          |
|    no      |           no          |
|    no      |           no          |
|    yes     |          yes          |

From this data, we can observe that when the patient has chronic heart failure, they are associated with the value "yes" in the "zab_leg_02" feature. Conversely, when the patient does not have chronic heart failure, they are associated with the value "no" in the "zab_leg_02" feature.

Therefore, we can create the following dictionary to summarize the relationship:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```

This dictionary shows that when the target variable is "no" (indicating no chronic heart failure), the possible value of the "zab_leg_02" feature is only "no". Similarly, when the target variable is "yes" (indicating chronic heart failure), the possible value of the "zab_leg_02" feature is only "yes".

Note that if there were other values of "zab_leg_02" for either target class, we would include them in the respective lists in the dictionary. However, based on the given information, these are the only values we can conclude from the data.