I have a Remaining Useful Life (RUL) prediction problem that I want to solve. When I added two or more features as inputs to my ANN, the accuracy of my ANN has been decreased. More precisely, I've added features like RMS or KURTOSIS (or both). I was expecting the system to improve, but it is getting worse.
Why might this be happening? What are the potential reasons for this degradation in performance?
I know that when we added more nodes in layers (like hidden layers), overfitting can happen. Would that be related to my problem: using more than two features?