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Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicity

2024-05-26

Abstract excerpt

<h4>Purpose</h4> To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability utilizing clinical data obtained from two distinct disease sites, and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled dataset. <h4>Methods and Materials</h4> We obtained 3 sets of radiation toxicity data...

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Literature Corpus work
7d9e2e19-2d3b-593f-a9c9-79e1e6675311
DOI
10.1101/2024.05.24.24307747
Open publication

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Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicityDOI 10.1101/2024.05.24.24307747
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