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

Saraee, M and Keane, J 2007, 'Using T3, an improved decision tree classifier, for mining stroke-related medical data' , Methods of Information in Medicine, 46 (5) , pp. 523-529.

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    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

    Item Type: Article
    Themes: Health and Wellbeing
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: Methods of Information in Medicine
    Publisher: Schattauer
    Refereed: Yes
    ISSN: 0026-1270
    Related URLs:
    Depositing User: Dr Mo Saraee
    Date Deposited: 21 Oct 2011 12:16
    Last Modified: 20 Aug 2013 18:15
    URI: http://usir.salford.ac.uk/id/eprint/18597

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