Title: Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy
Authors: Jayasurya, K ×
Fung, G
Yu, S
Dehing-Oberije, C
De Ruysscher, Dirk
Hope, A
De Neve, W
Lievens, Yolande
Lambin, P
Dekker, A. L. A. J #
Issue Date: Apr-2010
Publisher: Amer assoc physicists medicine amer inst physics
Series Title: Medical physics vol:37 issue:4 pages:1401-1407
Abstract: Purpose: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing.
ISSN: 0094-2405
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory of Experimental Radiotherapy
× corresponding author
# (joint) last author

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