Based on prior knowledge, it is difficult to directly infer the relationship between the feature "TIME_B_S" and the presence of chronic heart failure. However, we can analyze the distribution of "TIME_B_S" values for patients with and without chronic heart failure.

Here is the analysis:

- Patients with chronic heart failure (target class "yes") may have varying durations of time elapsed from the beginning of the attack to hospital admission.
- Patients without chronic heart failure (target class "no") may also have varying durations of time elapsed from the beginning of the attack to hospital admission.

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

```json
{
	"no": ["2-4 hours", "less than 2 hours", "4-6 hours", "6-8 hours", "8-12 hours", "more than 3 days", "more than 2 days", "12-24 hours", "more than 1 days"],
	"yes": ["2-4 hours", "less than 2 hours", "4-6 hours", "6-8 hours", "8-12 hours", "more than 3 days", "more than 2 days", "12-24 hours", "more than 1 days"]
}
```

Note: In this case, since we cannot determine which values of "TIME_B_S" are hard to predict, we include the full list of possible values for both target classes.