Based on the feature description and task, we want to analyze the relationship between the feature "K_SH_POST" (Cardiogenic shock at the time of admission to intensive care unit) and the presence of chronic heart failure. 

To analyze this relationship, we need to examine the values of the feature for each target class (yes or no) and determine if there is any pattern or association.

Here is the analysis of the relationship between the feature "K_SH_POST" and the presence of chronic heart failure:

- If the value of "K_SH_POST" is "no":
  - Possible values of feature "K_SH_POST" for target class no: ['no']
  - Explanation: This indicates that there is no occurrence of cardiogenic shock at the time of admission to the ICU. However, this feature alone does not directly indicate the presence or absence of chronic heart failure.

- If the value of "K_SH_POST" is "yes":
  - Possible values of feature "K_SH_POST" for target class yes: ['yes']
  - Explanation: This indicates that there is an occurrence of cardiogenic shock at the time of admission to the ICU. However, similar to the previous case, this feature alone does not directly indicate the presence or absence of chronic heart failure.

Based on this analysis, we can create the following dictionary:

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

Note: Since the feature "K_SH_POST" only has two possible values ('no' and 'yes'), it is not necessary to include values that are hard to predict in the dictionary. The lists of values for each target class should not be empty.