ABSTRACT
With more and more complex, big datasets being used in health disparity research, there is a growing interest in the role that machine learning can play in helping to understand underlying patterns and structure. This chapter discusses some important machine learning tools which have been gaining favor amongst health disparity researchers including tree-based approaches, random forests, shrinkage estimates like ridge regression and the lasso and finally, deep learning. Underlying methods are described with increasing mathematical rigor in this chapter and illustrative examples are displayed throughout.
