I have some data for medical diagnosis, consisting of some rules about relationship of diseases and their symptoms, for example disease D1 frequently has symptom S1 or disease D2 rarely has symptom S1. Given some symptoms, I want to somehow calculate most probable diseases based on this data.I thought about using a naive bayes model, but using that needs some assumptions about disease prevalence or probability of some symptoms given a disease that are not present in my rules set. Is there an standard way of doing this kind of analysis? For example are fuzzy systems used in this situation?NOTE: I think that in the cases where relation between a disease and a symptom is not explicitly stated as a rule it is an indication of either independence or a very low probability of the symptom given that disease.
Can inferencing come from incomplete rule sets?
naive bayes classifier;association rules;fuzzy logic