  In many healthcare settings, intuitive decision rules for risk stratification
can help effective hospital resource allocation. This paper introduces a novel
variant of decision tree algorithms that produces a chain of decisions, not a
general tree. Our algorithm, $\alpha$-Carving Decision Chain (ACDC),
sequentially carves out "pure" subsets of the majority class examples. The
resulting chain of decision rules yields a pure subset of the minority class
examples. Our approach is particularly effective in exploring large and
class-imbalanced health datasets. Moreover, ACDC provides an interactive
interpretation in conjunction with visual performance metrics such as Receiver
Operating Characteristics curve and Lift chart.
