I want to recommend one of three possible treatments for a patient, based on his blood values A, B and C.
To solve this task, I have constructed a supervised feed-forward NN with back-propagation (input values = blood values | output values = treatments).
My training data-set contains medical records for past treatments. A blood value between [0,1] is good, above is bad. It looks like this:
As you can see, some patients' blood values improved after the treatment (patient 001). Others partially improved (patient 002). Equally, there are some patients, whose blood values worsened (patient 003).
In training data pre-processing, one would delete patients 002 and 003, since patient 001 is the only "good" example.
Is there any possibility to teach my network to not follow the "bad" example of patient 003? Or is this network topology not capable of doing so at all?