Predicting breast screening attendance using machine learning techniques

V. Baskaran, A. Guergachi, Rajeev Bali, Raouf Naguib

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)
    42 Downloads (Pure)


    Machine learning-based prediction has been effec6
    tively applied for many healthcare applications. Predicting breast
    7 screening attendance using machine learning (prior to the actual
    8 mammogram) is a new field. This paper presents new predictor
    9 attributes for such an algorithm. It describes a new hybrid algo10
    rithm that relies on back-propagation and radial basis function11
    based neural networks for prediction. The algorithm has been de12
    veloped in an open source-based environment. The algorithm was
    13 tested on a 13-year dataset (1995–2008). This paper compares the
    14 algorithm and validates its accuracy and efficiency with different
    15 platforms. Nearly 80%accuracy and 88%positive predictive value
    16 and sensitivity were recorded for the algorithm. The results were
    17 encouraging; 40–50% of negative predictive value and specificity
    18 warrant further work. Preliminary results were promising and
    19 provided ample amount of reasons for testing the algorithm on a
    20 larger scale.
    Original languageEnglish
    Pages (from-to)251-259
    JournalIEEE Transactions on Information Technology in Biomedicine
    Issue number2
    Publication statusPublished - Mar 2011

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    • breast screening
    • cancer
    • machine learning
    • neural networks
    • prediction
    • screening attendance


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