The results of the ML algorithm need to be cleaned up and organized so that they can be used by the rest of the system.
No one (currently) can tell you which ML algorithm will work best on your problem and how its parameters should be chosen without a lot of study. Preconceived bias (e.g., that it should be biologically based, blah, blah) can be a killer here.
The best results typically come from combinations of ML algorithms through some kind of ensemble learning, so now your have the problem of choosing a good combination and choosing a lot more parameters.
All of the above need to work together in concert.
Certainly, it's not a bad idea to try to make this process better, but I wouldn't be expecting miracles too soon.