Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers
Medical Physics2018Vol. 45(7), pp. 3449–3459
Citations Over TimeTop 1% of 2018 papers
Timo M. Deist, Frank J. W. M. Dankers, Gilmer Valdés, Robin Wijsman, I‐Chow Hsu, Cary Oberije, Tim Lustberg, Johan van Soest, Frank Hoebers, Arthur Jochems, Issam El Naqa, Leonard Wee, Olivier Morin, David R. Raleigh, Wouter Bots, Johannes H.A.M. Kaanders, J. Belderbos, Margriet Kwint, Timothy D. Solberg, René Monshouwer, Johan Bussink, André Dekker, Philippe Lambin
Abstract
Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection.
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