Decision Trees for Decision Analysis (1,004 and 953 Patients)

Abstract

Decision trees are, so-called, non-metric or non-algorithmic methods adequate for fitting nominal and interval data (the latter either categorical or continuous). Better accuracy from decision trees is sometimes obtained by the use of a training sample (Chap. 8). This chapter is to assess whether decision trees can be appropriately applied to predict health risks and improvements.