Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk

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.

Name thesis:degree_name

Master of Arts (MA)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science and Engineering

Year dc:date.available

2009

Author dc:creator

• Schiller, Todd

Contributors dc:contributor

• Yixin Chen

• support vector machine
• radiation pneumonitis
• feature selection
• ensemble classifier
• data imbalance

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K7XK8CKP

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1931