Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases.

Biosimilar drugs do not always work well for rheumatic diseases. We obtain two imbalanced machine learning models to detect those cases, where the drug should be removed or where the activity of the disease increases from low to high. In our decision trees appear not previously studied variables, such as age, switching, or optimization.