Based on prior knowledge, we can analyze the relationship between the feature "ROE" (Erythrocyte sedimentation rate) and the target variable which is whether the patient shows chronic heart failure or not.

Given that we are trying to determine if the patient has chronic heart failure, we can assume that higher values of ROE might indicate the presence of chronic heart failure. however, it is important to note that this is a general assumption and might not hold true in all cases. Therefore, we need to analyze the data to determine the appropriate ranges of ROE values for each target class.

To generate the dictionary, let's assume a threshold of 10 for the presence of chronic heart failure. Any ROE value above 10 could indicate the presence of chronic heart failure, while values below 10 could indicate the absence of chronic heart failure.

Based on this analysis, the dictionary will be as follows:

```json
{
    "no": [4.2, 6.1, 8.3, 9.7, 9.8],
    "yes": [11.2, 12.5, 13.9, 15.3, 16.4]
}
```

In this dictionary, the "no" key represents the target class of patients without chronic heart failure, and the associated values represent some example ROE values for this class. Similarly, the "yes" key represents the target class of patients with chronic heart failure, and the associated values represent some example ROE values for this class.

Please note that these values are just examples and the actual values may vary based on the dataset and domain knowledge.