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Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence

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Green, Pervasive, and Cloud Computing (GPC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11484))

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Abstract

Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others.

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Author information

Authors and Affiliations

  1. Universidad Peruana de Ciencias Aplicadas, Lima, Peru

    Jesús Silva

  2. Universidad Tecnológica Centroamericana (UNITEC), San Pedro Sula, Honduras

    Omar Bonerge Pineda Lezama

  3. Universidad de la Costa, St. 58 #66, Barranquilla, Atlántico, Colombia

    Noel Varela & Luz Adriana Borrero

Authors
  1. Jesús Silva
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  2. Omar Bonerge Pineda Lezama
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  3. Noel Varela
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  4. Luz Adriana Borrero
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Corresponding author

Correspondence to Jesús Silva .

Editor information

Editors and Affiliations

  1. Federal University of Uberlândia, Uberlândia, Brazil

    Rodrigo Miani

  2. Federal University of Uberlândia, Uberlândia, Brazil

    Lasaro Camargos

  3. State University of Londrina, Londrina, Brazil

    Bruno Zarpelão

  4. Federico Santa María Technical University, San Joaquin, Chile

    Erika Rosas

  5. Federal University of Uberlândia, Uberlândia, Brazil

    Rafael Pasquini

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Silva, J., Lezama, O.B.P., Varela, N., Borrero, L.A. (2019). Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds) Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science(), vol 11484. Springer, Cham. https://doi.org/10.1007/978-3-030-19223-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-19223-5_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19222-8

  • Online ISBN: 978-3-030-19223-5

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