ABSTRACT
Clinical prediction is one of the most important branches of healthcare data analytics. In this
chapter, we will provide a relatively comprehensive review of the supervised learning methods that
have been employed successfully for clinical prediction tasks. Some of these methods such as lin-
ear regression, logistic regression, and Bayesian models are basic and widely investigated in the
statistics literature. More sophisticated methods in machine learning and data mining literature such
as decision trees and artificial neural networks have also been successfully used in clinical appli-
cations. In addition, survival models in statistics that try to predict the time of occurrence of a
particular event of interest have also been widely used in clinical data analysis.
