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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1615 |
| Date | 06 1900 |
| Creators | Lin, Hsiu-Chin |
| Contributors | Greiner, Russell (Computing Science), Baracos, Vickie (Oncology), Sander, Joerg (Computing Science) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1791424 bytes, application/pdf |
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