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I started using RapidMiner today and I think it's great!
What I’m looking for specifically is a method for the prediction of cancer patient survival based on multiple measurements from histological specimens. This can be done in Python’s "DeepSurv" and R’s "randomForestSRC" packages. I know a bit of R, so I got the latter to work but I struggle with Python and DeepSurv. DeepSurv may be more accurate. It would be interesting to compare the results obtained with these (and possibly other) packages.
So, my question is: Has anyone ever implemented a (patient) survival prediction model in RapidMiner?
The difference to the "normal" process is that one uses two variables to train the model on. One dichotomous variable, like “churn” in the example database, and a time variable (i.e., the survival time). One does not merely want to know IF someone died but also how long that took because depending on the time, the “IF-variable” can mean totally different things. E.g., someone died, but only after a very long time. That would obviously correspond to a good prognosis.
Any ideas would be very welcome.