Why use machine learning to solve a problem that you already know how to solve? The classical method is easier to derive, run and understand. Any failure cases can be easily reasoned about and can be clearly outlined and ameliorated if needed. Your proposed replacement needs lots of training data, a training cost that needs to be paid for any change in the data, a costlier runtime and unknown failure cases that we can't reason about at all. And if we hit any such failure cases the only thing we can do is change the data and cross our fingers. I don't see why learning-based methods would perform better here.