Is using unsupervised learning to setup supervised classification reasonable?
Is using unsupervised learning to setup supervised classification reasonable?


Absolutely. This is a common strategy in ML. As you said yourself, using information coming from the data itself has the benefit of being less biased.


Would it even also be possible to use unsupervised learning to create the labels?


Technically yes. Though, some clustering techniques require you to specify the number of clusters, which isn't helpful. As you said, if you can cluster data points in a satisfactory manner, you don't need supervised learning anymore. Also, indeed, if your scenario requires you to have an understanding of what differentiates the clusters, you may not be lucky depending on which clusters come out. They are not always interpretable.

What I would suggest would be to turn your classification problem into a regression problem. 1.0 could be most likely to cause the disease, 0.0, least likely. This way, you don't have to worry about how many labels you need in the first place.