Introduction to Decision Trees - Data Mining with Decision Trees: Theory and Applications - page 15

CPT

Type 1

Type 2

BTF

XRF

N

P

P

CTF

N

P

Fig. 1.7 Decision tree for medical applications.

Tree complexity is explicitly controlled by the stopping criteria and the
pruning method that are employed.

1.8.2 The Hierarchical Nature of Decision Trees

Another characteristic of decision trees is their hierarchical nature. Imagine
that you want to develop a medical system for diagnosing patients according
to the results of several medical tests. Based on the result of one test,
the physician can perform or order additional laboratory tests. Specifically,
Figure 1.7 illustrates the diagnosis process using decision trees of patients
who suffer from a certain respiratory problem. The decision tree employs
the following attributes: CT findings (CTF), X-ray findings (XRF), chest
pain type (CPT) and blood test findings (BTF). The physician will order
an X-ray if chest pain type is “1”. However, if chest pain type is “2”, then
the physician will not order an X-ray but rather, he or she will order a
blood test. Thus, medical tests are performed just when needed and the
total cost of medical tests is reduced.

1.9 Relation to Rule Induction

Decision tree induction is closely related to rule induction. Each path from
the root of a decision tree to one of its leaves can be transformed into a rule