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Learning Accurate Regressors for Predicting Survival Times of Individual Cancer Patients

  • Author / Creator
    Lin, Hsiu-Chin
  • Standard survival analysis focuses on population-based studies. The objective of our work, survival prediction, is different: to find the most accurate model for predicting the survival times for each individual patient. We view this as a regression problem, where we try to map the features for each patient to his/her survival time. This is challenging in medical data due to the presence of irrelevant features, outliers, and missing class labels. Our approach consists of two major steps: (1) apply various grouping methods to segregate patients, and (2) apply different regression to each sub-group we obtained from the first step. We focus our experiments on a data set of 2402 patients (1260 censored). Our final predictor can obtain an average relative absolute error < 0.54. The experimental results verify that we can effectively predict survival times with a combination of statistical and machine learning approaches.

  • Subjects / Keywords
  • Graduation date
    2011-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3ZP5B
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Greiner, Russell (Computing Science)
  • Examining committee members and their departments
    • Sander, Joerg (Computing Science)
    • Baracos, Vickie (Oncology)