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A Nearest Neighbor Approach to Build a Readable Risk Score for Breast Cancer

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Real World Data Mining Applications

Part of the book series: Annals of Information Systems ((AOIS,volume 17))

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Abstract

According to the World Health Organization, starting from 2010, cancer has become the leading cause of death worldwide. Prevention of major cancer localizations through a quantified assessment of risk factors is a major concern in order to decrease their impact in our society. Our objective is to test the performances of a modeling method that answers to needs and constraints of end users. In this article, we follow a data mining process to build a reliable assessment tool for primary breast cancer risk. A k-nearest-neighbor algorithm is used to compute a risk score for different profiles from a public database. We empirically show that it is possible to achieve the same performances as logistic regressions with less attributes and a more easily readable model. The process includes the intervention of a domain expert, during an offline step of the process, who helps to select one of the numerous model variations by combining at best, physician expectations and performances. A risk score made of four parameters: age, breast density, number of affected first degree relatives and breast biopsy, is chosen. Detection performance measured with the area under the ROC curve is 0.637. A graphical user interface is presented to show how users will interact with this risk score.

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Author information

Authors and Affiliations

  1. Statlife company, Institut Gustave Roussy, 114 rue Édouard Vaillant, 94805, Villejuif Cedex, France

    Émilien Gauthier & Stéphane Ragusa

  2. UMR CNRS 6285 Lab-STICC, Institut Telecom, Telecom Bretagne, Technopôle Brest Iroise CS 83818, 29238, Brest Cedex 3, France

    Émilien Gauthier, Laurent Brisson & Philippe Lenca

  3. Université Européenne de Bretagne, Bretagne, France

    Émilien Gauthier, Laurent Brisson & Philippe Lenca

Authors
  1. Émilien Gauthier
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  2. Laurent Brisson
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  3. Philippe Lenca
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  4. Stéphane Ragusa
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Corresponding author

Correspondence to Émilien Gauthier .

Editor information

Editors and Affiliations

  1. Research & Advanced Engineering, Ford Motor Company, Dearborn, Michigan, USA

    Mahmoud Abou-Nasr

  2. Universität Hamburg Inst. Wirtschaftsinformatik, Hamburg, Germany

    Stefan Lessmann

  3. Universität Hamburg Inst. Wirtschaftsinformatik, Hamburg, Germany

    Robert Stahlbock

  4. Deptartment of Computer & Information Science, Fordham University, Bronx, New York, USA

    Gary M. Weiss

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Gauthier, É., Brisson, L., Lenca, P., Ragusa, S. (2015). A Nearest Neighbor Approach to Build a Readable Risk Score for Breast Cancer. In: Abou-Nasr, M., Lessmann, S., Stahlbock, R., Weiss, G. (eds) Real World Data Mining Applications. Annals of Information Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07812-0_13

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