Based on the feature description and the task, we need to analyze how the feature "lat_im" relates to the presence of chronic heart failure (target variable).

To analyze the relationship, we will examine the different categories of the "lat_im" feature and determine which categories are more likely to be associated with chronic heart failure.

Based on prior knowledge, the following analysis can be conducted:

1. If the "lat_im" category is "QRS has no changes", it is less likely to indicate chronic heart failure. This category may indicate normal myocardial function.

2. If the "lat_im" category is "there is no infarct in this location", it is less likely to indicate chronic heart failure. This category suggests the absence of an infarction in the lateral region, which is not directly related to chronic heart failure.

3. If the "lat_im" category is "QRS is like Qr-complex", it is less likely to indicate chronic heart failure. This category may suggest a specific ECG morphology but doesn't strongly imply chronic heart failure.

4. If the "lat_im" category is "QRS is like QS-complex" or "QRS is like QR-complex", it is more likely to indicate chronic heart failure. These categories suggest specific ECG changes in the lateral leads and are often associated with infarction and subsequent complications such as chronic heart failure.

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

```json
{
	"no": ["QRS has no changes", "there is no infarct in this location", "QRS is like Qr-complex"],
	"yes": ["QRS is like QS-complex", "QRS is like QR-complex"]
}
```

Note: These are general insights based on the information provided. The analysis may vary depending on the specific dataset and its characteristics.