Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altekruse, S.F., Kosary, C.L., Krapcho, M., Neyman, N., Aminou, R., Waldron, W., Ruhl, J., Howlader, N., Tatalovich, Z., Cho, H., Mariotto, A., Eisner, M.P., Lewis, D.R., Cronin, K., Feuer, E.J., Stinchcomb, D.G., Edwards, B.K. (eds.): SEER Cancer Statistics Review, 1975-2007, National Cancer Institute. Bethesda, MD, http://seer.cancer.gov/csr/1975_2007/; based on November 2009 SEER data submission, posted to the SEER web site 2010
  2. 2.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  3. 3.
    Honda, K., Hayashida, Y., Umaki, T., Okusaka, T., Kosuge, T., Kikuchi, S., Endo, M., Tsuchida, A., Aoki, T., Itoi, T., Moriyasu, F., Hirohashi, S., Yamada, T.: Possible detection of pancreatic cancer by plasma protein profiling. Cancer Res. 65(22), 10613–10622 (2005)CrossRefGoogle Scholar
  4. 4.
    Ge, G., Wong, G.W.: Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles. BMC Bioinformatics 9, 275 (2008)CrossRefGoogle Scholar
  5. 5.
    Floyd, S., Alvarez, S.A., Ruiz, C., Hayward, J., Sullivan, M., Tseng, J., Whalen, G.: Improved survival prediction for pancreatic cancer using machine learning and regression, Society for the Surgery of the Alimentary Tract 48th Annual Meeting (SSAT 2007), Washington DC, USA (May 19-23, 2007)Google Scholar
  6. 6.
    Hayward, J., Alvarez, S.A., Ruiz, C., Sullivan, M., Tseng, J., Whalen, G.: Knowledge discovery in clinical performance of cancer patients. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2008), Philadelphia, PA, USA (November 3-5, 2008)Google Scholar
  7. 7.
    Hayward, J., Alvarez, S.A., Ruiz, C., Sullivan, M., Tseng, J., Whalen, G.: Machine Learning of Clinical Performance in a Pancreatic Cancer Database. In: Kim, S. (ed.) special issue on Data Mining Approaches to the Study of Disease Genes and Proteins, Artificial Intelligence in Medicine 49(3), 187–195 (2010)Google Scholar
  8. 8.
    Floyd, S., Ruiz, C., Alvarez, S.A., Tseng, J., Whalen, G.: Model Selection Meta-Learning for the Prognosis of Pancreatic Cancer, full paper. In: Proc. Third International Conference on Health Informatics (HEALTHINF 2010), in Conjunction with the Third International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2010), Valencia, Spain, January 20-23, pp. 29–37 (2010)Google Scholar
  9. 9.
    Qu, Y., Adam, B.L., Yasui, Y., Ward, M.D., Cazares, L.H., Schellhammer, P.F., Feng, Z., Semmes, O.J., Wright Jr., G.L.: Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin. Chem. 48, 1835–1843 (2002)Google Scholar
  10. 10.
    Bhanot, G., Alexe, G., Venkataraghavan, B., Levine, A.J.: A robust meta-classification strategy for cancer detection from MS data. Proteomics 6(2), 592–604 (2006)CrossRefGoogle Scholar
  11. 11.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Wolpert, D.H.: Stacked generalization. Neural Networks 5(2), 241–259 (1992)CrossRefGoogle Scholar
  14. 14.
    Lawless, J.F.: Statistical models and methods for lifetime data, 2nd edn. Wiley, Chichester (2003)zbMATHGoogle Scholar
  15. 15.
    Witten, I.H., Frank, E.: Data Mining, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)zbMATHGoogle Scholar

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.

Personalised recommendations