Based on prior knowledge, there is a potential relationship between the feature "ritm_ecg_p_07" and the target variable "chronic heart failure." 

To analyze the relationship, we can look at previous studies or medical knowledge to understand if a certain ECG rhythm at the time of admission to the hospital (sinus with a heart rate above 90) is associated with chronic heart failure.

Let's assume that sinus rhythm with a heart rate above 90 is commonly observed in patients with chronic heart failure. In this case, we would expect the presence of chronic heart failure to be more likely when the feature "ritm_ecg_p_07" is set to 'yes'.

Based on this analysis, we can build the dictionary:

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

Since the feature "ritm_ecg_p_07" has only two possible categories, we include both 'no' and 'yes' in the dictionary. However, if there were additional categories that were hard to predict, we would not include them unless they are associated with a specific target class, ensuring that the lists for each target class are not empty.