I am building a supervised machine learning model which (for example) predicts heart failure (yes/no). I have two datasets from 2 different labs A and B, which both have decent distribution, aka it's not like A has way more young people than B, but somehow A has much lower rate of heart failure.

Having created a separate model for each, they each achieve around 90% or higher accuracy, but my aim is to make one model which can use a concatenated dataset containing information from A and B. Currently this combined model gets 75% accuracy for samples from B and 90% for A.

What I have tried:

  • sample in different ways / create 'even' datasets
  • add feature indicating which lab
  • normalise numerical features
  • create categories for numerical features

I'm aware the overall results would of course not become as good as the individual models, but how can I make the platforms at least get more similar results? I appreciate any advice / methods I could try out to tackle this type of problem!!


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