AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

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KNOWLEDGE ENGINEERING AND DATA MINING
Figure 9.47
Gain charts of Class A
random sample of the population. The results clearly demonstrate the advantages
of the tree constructed with the Gini splits.
Can we extract rules from a decision tree?
The pass from the root node to the bottom leaf reveals a decision rule. For
example, a rule associated with the right bottom leaf in Figure 9.46(a) can be
represented as follows:
if
and
and
then
(Predictor 1 ¼ no)
(Predictor 4 ¼ no)
(Predictor 6 ¼ no)
class ¼ Class A
Case study 10: Decision trees for data mining
I have the results of a community health survey, and I want to understand
which people are at a greater risk of having high blood pressure. Will
decision trees work for this problem?
A typical task for decision trees is to determine conditions that may lead to
certain outcomes. This makes decision trees a good choice for profiling people
with high blood pressure, and community health surveys can provide us with
the necessary data.
High blood pressure, also called hypertension, occurs when the body’s smaller
blood vessels narrow. This causes the heart to work harder to maintain the
pressure, and although the body can tolerate increased blood pressure for
months and even years, eventually the heart may fail.
Blood pressure can be categorised as optimal, normal or high. Optimal
pressure is below 120/80, normal is between 120/80 and 130/85, and a hypertension
is diagnosed when blood pressure is over 140/90. Figure 9.48 shows an
example of a data set used in a hypertension study.