Machine Learning Outperform Traditional Approaches in Predicting Clinically Significant Prostate Cancer

Machine Learning Outperform Traditional Approaches in Predicting Clinically Significant Prostate Cancer

Ordones, Flavio Vasconcelos ; Vermeulen, Lodewikus ; Hooshyari, Ali ; Scholtz, David ; Kawano, Paulo ; de Andrade, Gustavo Modelli ; Barros, Abner ; Gilling, Peter
Identifier: https://hdl.handle.net/2292/68773
Issue Date: 2024-05
Reference: (2024). AUA Annual Meeting 2024, San Antonio, TX, USA, 03 May 2024 - 06 May 2024. Journal of Urology. Lippincott, Williams & Wilkins. 211: e503-e503. May 2024
Rights: Copyright: American Urological Association Education and Research, Inc.
Rights (URI): https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm

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DOI: 10.1097/01.ju.0001008936.35187.0b.02
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