Author's Department/Program
Computer Science and Engineering
Language
English (en)
Date of Award
1-1-2009
Degree Type
Thesis
Degree Name
Master of Arts (MA)
Chair and Committee
Yixin Chen
Abstract
Patients undergoing radiation therapy can develop a potentially fatal inflammation of the lungs known as radiation pneumonitis: RP). In practice, modeling RP factors is difficult because existing data are under-sampled and imbalanced. Support vector machines: SVMs), a class of statistical learning methods that implicitly maps data into a higher dimensional space, is one machine learning method that recently has been applied to the RP problem with encouraging results. In this thesis, we present and evaluate an ensemble SVM method of modeling radiation pneumonitis. The method internalizes kernel/model parameter selection into model building and enables feature scaling via Olivier Chapelle's method. We show that the ensemble method provides statistically significant increases to the cross-folded area under the receiver operating characteristic curve while maintaining model parsimony. Finally, we extend our model with John C. Platt's method to support non-binary outcomes in order to augment clinical relevancy.
Recommended Citation
Schiller, Todd, "Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk" (2009). All Theses and Dissertations (ETDs). 932.
https://openscholarship.wustl.edu/etd/932
Comments
Permanent URL: http://dx.doi.org/10.7936/K7XK8CKP