In this case, those parameters would be the image data and whatever health parameter is of interest (e.g. white blood cell count). My initial skepticism, perhaps that of the parent comments as well, has more to do with whether the measurements are of high enough quality for any reliable analysis to be done. The app doesn't seem to require any background or contextual data either (though I haven't verified this). If not, false positives and negatives could be problematic.
Anyway, machine learning isn't a form of magic that can transform data with no meaningful sensitivity to something into a something that is sensitive to it.
More data is always nice, but typically you see accuracy level off (diminishing returns). ML is a constant process of improving your data, increasing the amount of available data (not the same as improving your data), improving your features, and improving your model. No one thing is sufficient.