Prediction of Pancreatic Cancer Survival through Automated Selection of Predictive Models

  • Stuart Floyd
  • Carolina Ruiz
  • Sergio A. Alvarez
  • Jennifer Tseng
  • Giles Whalen
Part of the Communications in Computer and Information Science book series (CCIS, volume 127)


Cancer survival forecasting may be attempted using models constructed through predictive techniques of various kinds, including statistical multivariate regression and machine learning. However, no single such technique provides the best predictive performance in all cases. We present an automated meta-learning approach that learns to predict the best performing technique for each individual patient. The individually selected technique is then used to forecast survival for the given patient. We evaluate the proposed approach over a database of retrospective records of pancreatic cancer surgical resections.


Pancreatic Cancer Support Vector Machine Feature Selection Machine Learning Technique Target Attribute 
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 2011

Authors and Affiliations

  • Stuart Floyd
    • 1
  • Carolina Ruiz
    • 1
  • Sergio A. Alvarez
    • 2
  • Jennifer Tseng
    • 3
  • Giles Whalen
    • 3
  1. 1.Department of Computer ScienceWorcester Polytechnic InstituteWorcesterU.S.A.
  2. 2.Department of Computer ScienceBoston CollegeChestnut HillU.S.A.
  3. 3.Department of Surgical OncologyUniversity of Massachusetts Medical SchoolWorcesterU.S.A.

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