Cargando…
Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information
PURPOSE: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information. DATA AND METHODS: Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conduct...
Autores principales: | Kakileti, Siva Teja, Manjunath, Geetha, Dekker, Andre, Wee, Leonard |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
West Asia Organization for Cancer Prevention
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771951/ https://www.ncbi.nlm.nih.gov/pubmed/32856859 http://dx.doi.org/10.31557/APJCP.2020.21.8.2307 |
Ejemplares similares
-
A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital
por: Bansal, Richa, et al.
Publicado: (2023) -
Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women
por: Kakileti, Siva Teja, et al.
Publicado: (2020) -
Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19
por: Antony, Mariamma, et al.
Publicado: (2023) -
Inaccurate level of intervertebral space estimated by palpation: The ultrasonic revelation
por: Parate, LH, et al.
Publicado: (2016) -
Borderline features are associated with inaccurate trait self-estimations
por: Morey, Leslie C
Publicado: (2014)