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ICMLA
2009

Improving Clinical Relevance in Ensemble Support Vector Machine Models of Radiation Pneumonitis Risk

8 years 8 months ago
Improving Clinical Relevance in Ensemble Support Vector Machine Models of Radiation Pneumonitis Risk
Patients undergoing thoracic radiation therapy can develop radiation pneumonitis (RP), a potentially fatal inflammation of the lungs. Support vector machines (SVMs), a statistical machine learning method, have recently been used to build binary-outcome RP prediction models with promising results. In this work, we (1) introduce a feature-ranking selection step to improve the parsimony of our previous ensemble SVM model (2) show that ensembles of SVMs provide a statistically significant performance improvement in the area under the cross-validated receiver operating curve and (3) apply Platt's tuning to the component SVMs to generate probability estimates in order to augment clinical relevance.
Todd W. Schiller, Yixin Chen, Issam El-Naqa, Josep
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICMLA
Authors Todd W. Schiller, Yixin Chen, Issam El-Naqa, Joseph O. Deasy
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