A lot of what machine learning offers is beyond correlation and more about interaction between variables to get a result. So think multivariate analyses. If you can do a multivariate analysis to get to something that is statistically significant for a certain disease, then it would probably be worth checking out.
Think of it this way: machine learning is all about grabbing features from what we can normally say "duh its right there that's what is causing it." but in an automated manner. So how do we make the rules for it?
We need many, MANY, examples. If you can provide CONCRETE examples for each occurrence, then you MAY have a chance at giving it some sort of predictive capability.
The more important issue is HOW you plan to extract these features, the things that make you go "duh, that's whats causing it." So focus on this last part, and the rest will come easier.
Think of it this way: machine learning is all about grabbing features from what we can normally say "duh its right there that's what is causing it." but in an automated manner. So how do we make the rules for it?
We need many, MANY, examples. If you can provide CONCRETE examples for each occurrence, then you MAY have a chance at giving it some sort of predictive capability.
The more important issue is HOW you plan to extract these features, the things that make you go "duh, that's whats causing it." So focus on this last part, and the rest will come easier.