From the given feature "TIME_B_S", we need to analyze its relationship with the target variable "chronic heart failure" for the patient's myocardial infarction complications data.

To do this, we can start by examining the distribution of different TIME_B_S values for patients with and without chronic heart failure. By comparing the distribution of TIME_B_S values between the two target classes, we can observe any patterns or tendencies that might exist.

Once we have analyzed the relationship, we can create the dictionary as requested. Here is an example of how the dictionary might look:

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

In this example, the target class "no" (indicating no chronic heart failure) is associated with shorter time intervals since the beginning of the attack of coronary heart disease (CHD) to the hospital, such as 2-4 hours, less than 2 hours, 4-6 hours, 6-8 hours, and 8-12 hours. On the other hand, the target class "yes" (indicating chronic heart failure) is associated with longer time intervals, such as more than 3 days, more than 2 days, 12-24 hours, and more than 1 day.

Please note that the values provided in the example are for explanation purposes only and may not accurately represent the actual relationship between the feature and the target. The actual values should be determined based on an analysis of the dataset.