What is the approximate proportion of the classes in the dataset?
Have you tried changing the no of hidden layers and activation function? What library are you using?

You have to have a non linear activation function at each layer otherwise your model won't learn any non linear relationship in your data. It will simply classify with a linear decision boundary.

Can anyone explain how exactly kernel functions extract information from higher dimensional data without actually transforming it? What exactly is the scalar output? And how is it used for further classification???