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Permanent link (DOI): https://doi.org/10.7939/R3ZP5B

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Learning Accurate Regressors for Predicting Survival Times of Individual Cancer Patients Open Access

Descriptions

Other title
Subject/Keyword
Medical Informatics
Machine Learning
Survival Prediction
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Lin, Hsiu-Chin
Supervisor and department
Greiner, Russell (Computing Science)
Examining committee member and department
Baracos, Vickie (Oncology)
Sander, Joerg (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2010-11-04T16:38:17Z
Graduation date
2011-06
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3ZP5B
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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