Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostate-specific antigen velocity

BACKGROUND: Prostate-specific antigen (PSA)–based screening for prostate cancer has been widely performed, but its accuracy is unsatisfactory. To improve accuracy, building an effective statistical model using machine learning methods (MLMs) is a promising approach. METHODS: Data on continuous chang...

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Detalles Bibliográficos
Autores principales: Nitta, Satoshi, Tsutsumi, Masakazu, Sakka, Shotaro, Endo, Tsuyoshi, Hashimoto, Kenichiro, Hasegawa, Morikuni, Hayashi, Takayuki, Kawai, Koji, Nishiyama, Hiroyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asian Pacific Prostate Society 2019
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713794/
https://www.ncbi.nlm.nih.gov/pubmed/31485436
http://dx.doi.org/10.1016/j.prnil.2019.01.001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713794/
https://www.ncbi.nlm.nih.gov/pubmed/31485436
http://dx.doi.org/10.1016/j.prnil.2019.01.001

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