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2019 | OriginalPaper | Chapter

Application of a Rule-Based Classifier to Data Regarding Radiation Toxicity in Prostate Cancer Treatment

Authors : Juan L. Domínguez-Olmedo, Jacinto Mata, Victoria Pachón, Jose L. Lopez Guerra

Published in: Biomedical Engineering Systems and Technologies

Publisher: Springer International Publishing

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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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Metadata
Title
Application of a Rule-Based Classifier to Data Regarding Radiation Toxicity in Prostate Cancer Treatment
Authors
Juan L. Domínguez-Olmedo
Jacinto Mata
Victoria Pachón
Jose L. Lopez Guerra
Copyright Year
2019
Book
Biomedical Engineering Systems and Technologies

Print ISBN: 978-3-030-29195-2

Electronic ISBN: 978-3-030-29196-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-29196-9_20

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