I am using Logistic Regression (LR) to obtain Coronary Artery Disease CAD probability equation. The dataset has 16 candidate predictors, all continuous. There are two groups, CAD patient group (70 subjects), and an age-matched control group AMC (~ 70 subjects). Given that the control group is age-matched, I have a feeing that this is impacting the performance of the model negatively.
To test this, I have divided the original dataset randomly into 50% training set, and a testing set. I then included an additional group, Young Healthy Control (YHC) (who represent the healthy population more accurately) to the training set. Once a model has been selected, I applied this model to predict on the test set (which does not include YHC). The performance showed improvement compared with the original approach.
Is this approach acceptable, given that primary objective of the CAD equation is to predict CAD on a population similar to the original dataset?