Knowledge discovery from a breast cancer database

  • Subramani Mani
  • Michael J. Pazzani
  • John West
Knowledge Acquisition and Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)


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.


Breast Cancer Machine Learn Algorithm Optimal Treatment Plan Medical Record Database Learn Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Subramani Mani
    • 1
  • Michael J. Pazzani
    • 1
  • John West
    • 2
  1. 1.Department of Information and Computer ScienceUniversity of California at IrvineIrvine
  2. 2.Breast Care CenterOrange

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