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Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers
Author(s) -
Deist Timo M.,
Dankers Frank J. W. M.,
Valdes Gilmer,
Wijsman Robin,
Hsu IChow,
Oberije Cary,
Lustberg Tim,
Soest Johan,
Hoebers Frank,
Jochems Arthur,
El Naqa Issam,
Wee Leonard,
Morin Olivier,
Raleigh David R.,
Bots Wouter,
Kaanders Johannes H.,
Belderbos José,
Kwint Margriet,
Solberg Timothy,
Monshouwer René,
Bussink Johan,
Dekker Andre,
Lambin Philippe
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12967
Subject(s) - random forest , artificial intelligence , machine learning , computer science , support vector machine , decision tree , classifier (uml) , brier score , feature selection , gradient boosting , discriminative model , artificial neural network , logistic regression
Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.