Using T3, an improved decision tree classifier, for mining stroke-related medical data

Saraee, MH; Keane, J

Abstract

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 management and 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

Citation

Saraee, M., & Keane, J. (2007). Using T3, an improved decision tree classifier, for mining stroke-related medical data. Methods of Information in Medicine, 46(5), 523-529. https://doi.org/10.1160/ME0317

• Journal Article Type: Article
• Publication Date: Jan 1, 2007
• Deposit Date: Oct 21, 2011
• Publicly Available Date: Apr 5, 2016
• Journal: Methods of Information in Medicine
• Print ISSN: 0026-1270
• Publisher: Schattauer
• Peer Reviewed: Peer Reviewed
• Volume: 46
• Issue: 5
• Pages: 523-529
• DOI: https://doi.org/10.1160/ME0317
• Publisher URL: http://dx.doi.org/10.1160/ME0317
• Related Public URLs: http://www.schattauer.de/de/magazine/uebersicht/zeitschriften-a-z/methods/contents/archive/issue/673/manuscript/8823/download.html