I have clinical data, where patients are grouped into three groups. However, when I classify group 1 vs 2 (based on their behavioral data), and apply the trained classifier onto group 3, group 3 is almost always predicted just as group 1.
Now I am interested into understanding if it really makes sense to divide the patients into three groups, and if not two groups would be sufficient. I understand that clustering would be one such method, but I am interested if there are not other, possibly classification, approaches, where it has not be decided in advance how many groups (clusters) there are.
The classifier I am using is a linear SVM. The data is based on how subjects are performing on a bimanual task, for instance what force each hand is applying, what the ratio of these forces are, how these forces change in different conditions. I also do have data on age and gender, though performance is more predictive for the conditions. Predicted are three categories of a motor-disease condition.
Any feedback appreciated