Comparing data mining with ensemble classification of breast cancer masses in digital mammograms

Shima Ghassempour, Peter McLeod, Brijesh Verma, Anthony Maeder

    Research output: Contribution to conferencePaper

    4 Citations (Scopus)

    Abstract

    Medical diagnosis sometimes involves detecting subtle indications of a disease or condition amongst a background of diverse healthy individuals. The amount of information that is available for discovering such indications for mammography is large and has been growing at an exponential rate, due to population wide screening programmes. In order to analyse this information data mining techniques have been utilised by various researchers. A question that arises is: do exible data mining techniques have comparable accuracy to dedicated classification techniques for medical diagnostic processes? This research compares a model-based data mining technique with a neural network classification technique and the improvements possible using an ensemble approach. A publicly available breast cancer benchmark database is used to determine the utility of the techniques and compare the accuracies obtained.

    Original languageEnglish
    Pages55-63
    Number of pages9
    Publication statusPublished - 1 Jan 2012
    EventAIH 2012 -
    Duration: 4 Dec 2012 → …

    Conference

    ConferenceAIH 2012
    Period4/12/12 → …

    Keywords

    • Breast cancer
    • Classification
    • Clustering
    • Digital mammography
    • Latent class analysis
    • Neural network

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  • Cite this

    Ghassempour, S., McLeod, P., Verma, B., & Maeder, A. (2012). Comparing data mining with ensemble classification of breast cancer masses in digital mammograms. 55-63. Paper presented at AIH 2012, .