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Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

  • Systems-Level Quality Improvement
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Abstract

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

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Acknowledgments

The authors would like to thank the Breast Cancer Digital Repository Consortium of University of Porto and in particular Doctor Mário Vaz and Doctor Miguel Guevara who allowed the access to the BCDR dataset.

This work is supported by FEDER Funds through COMPETE program and by National Funds through FCT under the project UID/EEA/00760/2013.

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Authors and Affiliations

  1. CINTESIS - Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal

    Joana Diz & Alberto Freitas

  2. GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering-Polytechnic of Porto, Porto, Portugal

    Goreti Marreiros

  3. CIDES - Department of Health Information and Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal

    Alberto Freitas

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  1. Joana Diz
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Correspondence to Joana Diz.

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Diz, J., Marreiros, G. & Freitas, A. Applying Data Mining Techniques to Improve Breast Cancer Diagnosis. J Med Syst 40, 203 (2016). https://doi.org/10.1007/s10916-016-0561-y

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