TY - GEN
T1 - Nonlinear kernel-based approaches for predicting normal tissue toxicities
AU - El Naqa, Issam
AU - Bradley, Jeffrey D.
AU - Deasy, Joseph O.
PY - 2008
Y1 - 2008
N2 - Since the early demonstration of the curative potential of radiation therapy for tumor sterilization, normal tissue toxicity continues to be dose limiting. Accurate prediction of patient's complication risk would allow personalization of treatment planning decisions. Nonlinear kernel methods can provide a robust framework for learning complex interactions between observed toxicities and treatment, anatomical, and patient-related variables. However, proper application of these powerful methods would require better understanding of a high-dimensional feature space that is spanned by all these variables. In this work, we investigate methods for visualization of this high-dimensional space and compare different approaches for extracting discriminant features. Our preliminary results demonstrate that principle component analysis is a valuable tool for visualizing high dimensional data and for determining proper kernel type. In addition, variable selection based on resampling methods within the logistic regression framework seemed to yield improved prediction performance compared to the recursive-feature elimination method.
AB - Since the early demonstration of the curative potential of radiation therapy for tumor sterilization, normal tissue toxicity continues to be dose limiting. Accurate prediction of patient's complication risk would allow personalization of treatment planning decisions. Nonlinear kernel methods can provide a robust framework for learning complex interactions between observed toxicities and treatment, anatomical, and patient-related variables. However, proper application of these powerful methods would require better understanding of a high-dimensional feature space that is spanned by all these variables. In this work, we investigate methods for visualization of this high-dimensional space and compare different approaches for extracting discriminant features. Our preliminary results demonstrate that principle component analysis is a valuable tool for visualizing high dimensional data and for determining proper kernel type. In addition, variable selection based on resampling methods within the logistic regression framework seemed to yield improved prediction performance compared to the recursive-feature elimination method.
UR - http://www.scopus.com/inward/record.url?scp=60649097321&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2008.126
DO - 10.1109/ICMLA.2008.126
M3 - Conference contribution
AN - SCOPUS:60649097321
SN - 9780769534954
T3 - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
SP - 539
EP - 544
BT - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
T2 - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Y2 - 11 December 2008 through 13 December 2008
ER -