overfitted model

hey guys I ran into a problem and I hoped that maybe I could get some help.

I trained a predictive model that can detect diabetes and I reached 91.6 accuracy via Random forest algorithm, but I am not sure that if my model is overfitted or not.

I read somewhere that one of the ways that I can tell if the model is overfitted or not is by comparing training datas accuracy and test datas accuracy. I used the scorer node to find out my training dataset score and it was 73 but my test dataset was 91.6. I also read somewhere that I can get insights by comparing f-measures and recalls and precision, But the thing is I don’t know which f-measures I have to compare. Do I have to compare the f-measures or recalls of two different rows of my test data set or should I compare the f-measures of my training dataset with f-measures of my test datasets?

I also used ROC curves and found out that my training dataset has a 0.8 AUC and my test dataset has a 0.73 AUC.

I will leave pictures of my workflow and my scorer nodes.

I would appreciate any help or explanation about any of the stuff I have problem with