Prediction of breast cancer survivability using ensemble algorithms
Vincent Adegoke; Daqing Chen; Ebad Banissi; Safia Barikzai · 2017
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms…
Excerpt shown for reference under fair use — read the full paper at the publisher.
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1109/sst.2017.8188699