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Knowledge discovery from a breast cancer database

  • Knowledge Acquisition and Learning
  • Conference paper
  • First Online:
Artificial Intelligence in Medicine (AIME 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1211))

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Abstract

We report on the use of various Machine Learning algorithms on an electronic database of breast cancer patients. The task was to predict breast cancer recurrence using a short subset of clinical attributes such as tumor presence, tumor size, invasive nature of tumor, number of lymph nodes involved, severity of lymphedema and stage of tumor. The predictive accuracy over fifty runs employing test sets not used to build the model were 63.4%(Cart), 63.9%(C45), 62.5%(C45rules), 66.4%(FOCL) and 68.3%(Naive Bayes). An extension of the model using additional features and larger datasets is contemplated.

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References

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Author information

Authors and Affiliations

  1. Department of Information and Computer Science, University of California at Irvine, 444 Computer Science Building, 92697-3425, Irvine, CA

    Subramani Mani & Michael J. Pazzani

  2. Breast Care Center, 1140 W.La Veta, 92668, Orange, CA

    John West MD

Authors
  1. Subramani Mani
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  2. Michael J. Pazzani
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  3. John West MD
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Editor information

Elpida Keravnou Catherine Garbay Robert Baud Jeremy Wyatt

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© 1997 Springer-Verlag Berlin Heidelberg

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Mani, S., Pazzani, M.J., West, J. (1997). Knowledge discovery from a breast cancer database. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029444

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  • DOI: https://doi.org/10.1007/BFb0029444

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62709-8

  • Online ISBN: 978-3-540-68448-0

  • eBook Packages: Springer Book Archive

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