Based on prior knowledge, we can analyze the relationship between the feature "ChestPainType" and the task of determining whether the patient has a heart disease or not.

Given that the feature "ChestPainType" is categorical with categories ['ATA', 'NAP', 'ASY', 'TA'], we can examine how these categories are distributed among the target classes.

To do this, we could analyze a dataset or study previous findings. Without specific data, we can make some general assumptions:

- "ATA" (Atypical Angina) chest pain type is more likely to be associated with heart disease.
- "NAP" (Non-Anginal Pain) chest pain type may have a moderate likelihood of being associated with heart disease.
- "ASY" (Asymptomatic) chest pain type would have a low likelihood of being associated with heart disease.
- "TA" (Typical Angina) chest pain type is more likely to be associated with heart disease.

Based on this analysis, we can create a dictionary with the relevant information:

```json
{
	"no": ["ASY"],
	"yes": ["ATA", "NAP", "TA"]
}
```

In this case, "no" represents the target class for which the coronary angiography does not show heart disease, and "yes" represents the target class for which the coronary angiography does show heart disease. The associated values are the possible values of the feature "ChestPainType" for each target class.

Please note that this analysis is based on general assumptions and without specific data, so the accuracy may vary in real-world scenarios.