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 limit complexity in ensemble SVM models (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 generate probability estimates from the component SVMs in order to augment clinical relevance.