The task is the following: given a training set of medical symptoms and an associated diagnosis, output a list of the most likely diagnosises for a combination of symptoms. As of now, a solution exists which makes use of association rule learning methods: we find rules on the attributes of the training dataset and infer the likely diagnosises and their probability from the confidence we have in said rules to hold true.
However, this method doesn't seem to be able to scale for large datasets because of the sheer number of different attributes ($10^4$) and possible classes ($10^3$). Hence my question: are association rules a viable solution for this problem? Are there any alternatives?