Summary
Objectives:
Medical data are a valuable resource from which novel and potentially useful knowledge
can be discovered by using data mining. Data mining can assist and support medical
decision making and enhance clinical managementand investigative research. The objective
of this work is to propose a method for building accurate descriptive and predictive
models based on classification of past medical data. We also aim to compare this method
with other well established data mining methods and identify strengths and weaknesses.
Method:
We propose T3, a decision tree classifier which builds predictive models based on
known classes, by allowing for a certain amount of misclassification error in training
in order to achieve better descriptive and predictive accuracy. We then experiment
with a real medical data set on stroke, and various subsets, in order to identify
strengths and weaknesses. We also compare performance with a very successful and well
established decision tree classifier.
Results:
T3 demonstrated impressive performance when predicting unseen cases of stroke resulting
in as little as 0.4% classification error while the state of the art decision tree
classifier resulted in 33.6% classification error respectively.
Conclusions:
This paper presents and evaluates T3, a classification algorithm that builds decision
trees of depth at most three, and results in high accuracy whilst keeping the tree
size reasonably small. T3 demonstrates strong descriptive and predictive power without
compromising simplicity and clarity. We evaluate T3 based on real stroke register
data and compare it with C4.5, a well-known classification algorithm, showing that
T3 produces significantly more accurate and readable classifiers.
Keywords
Knowledge-based systems - medical data mining - decision tree classification - computer-assisted
decision making
