Abstract
In this work we describe a rule-based classifier (DEQAR-CC), which employs a combination of selected rules after a two-phase training process, and without the need of a previous discretization for the numerical variables. It was compared in the application to a real imbalanced dataset regarding the toxicity during and after radiation therapy for prostate cancer. In this comparison with other predictive methods (rule-based, artificial neural networks, trees, Bayesian and logistic regression), DEQAR-CC showed a better global prediction performance than the rest of classifiers, in an evaluation regarding several performance measures and by using cross-validation. Finally, it was employed to obtain a predictive model for genitourinary toxicity, obtaining an interpretable classification scheme which simply combines two rules with two variables.
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Acknowledgments
The research presented in this paper was partially funded by the Regional Government of Andalusia (Junta de Andalucía) under grant number TIC-7629 and Spanish Ministry of Education and Science (Grant Number: TIN2009-14057-C03-03).
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Domínguez-Olmedo, J.L., Mata, J., Pachón, V., Lopez Guerra, J.L. (2019). Application of a Rule-Based Classifier to Data Regarding Radiation Toxicity in Prostate Cancer Treatment. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_20
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