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

  • Machine Learning
  • Survival Prediction
  • Medical Informatics

• Graduation date

  Spring 2011

• 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.