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Comparison of Feature Selection Methods for Predicting RT-Induced Toxicity
Francisco J. Núñez-Benjumea, Jesús Moreno-Conde, Sara González-García, Alberto Moreno-Conde, José L. López-Guerra, María J. Ortiz-Gordillo, Carlos L. Parra-Calderón
253 - 254
10.3233/978-1-61499-959-1-253
Studies in Health Technology and Informatics
Volume 258: ICT for Health Science Research
Abstract

This work addresses a scoping review of Feature Selection (FS) methods applied to a Lung Cancer dataset to elucidate parameters' relevance when predicting radiotherapy (RT) induced toxicity. Subsetting-based and Ranking-based FS methods were implemented along with 4 advanced classifiers to predict the onset of RT-induced acute esophagitis, cough, pneumonitis and dyspnea. Their prediction performance was measured in terms of the AUC for each model to find the best FS.

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