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Using Data Mining Techniques to Support Breast Cancer Diagnosis

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New Contributions in Information Systems and Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 353))

Abstract

More than ever, in breast cancer research, many computer aided diagnostic systems have been developed in order to reduce false-positives diagnosis. In this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnose. A reliable database with 410 images was used containing microcalcifications, masses and also normal tissue findings. We applied two feature extraction techniques, specifically the gray level co-occurrence matrix and the gray level run length matrix, and for classification purposes several data mining classifiers were also used. The results revealed great percentages of positive predicted value (approximately 70%) and very good accuracy values in terms of distinction of mammographic findings (>65%) and classification of BI-RADS® scale (>75%). The best predictive method and the best performance on the distinction of microcalcifications found was the Random Forest classifier.

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Author information

Authors and Affiliations

  1. Institute of Engineering-Polytechnic of Porto, Porto, Portugal

    Joana Diz

  2. GECAD - Knowledge Engineering and Decision Support Group, Institute of Engineering-Polytechnic of Porto, Porto, Portugal

    Goreti Marreiros

  3. Center for Health Technology and Services Research (CINTESIS) and, Department of Health Information and Decision Sciences (CIDES), Faculty of Medicine, University of Porto, Porto, Portugal

    Alberto Freitas

Authors
  1. Joana Diz
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  2. Goreti Marreiros
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  3. Alberto Freitas
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Corresponding author

Correspondence to Joana Diz .

Editor information

Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Portugal

    Alvaro Rocha

  2. Campus de Campolide, ISEGI, Lisboa, Portugal

    Ana Maria Correia

  3. DEIS, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

  4. DIS, Universidade do Minho, Guimarães, Portugal

    Luis Paulo Reis

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© 2015 Springer International Publishing Switzerland

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Diz, J., Marreiros, G., Freitas, A. (2015). Using Data Mining Techniques to Support Breast Cancer Diagnosis. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-319-16486-1_68

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  • DOI: https://doi.org/10.1007/978-3-319-16486-1_68

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16485-4

  • Online ISBN: 978-3-319-16486-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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