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- W3138313893 abstract "AbstractThe Wisconsin Breast Cancer Epidemiologic Simulation Model (Tsai et al. in J Big Data 2:21, 2015) is a stochastic simulation model that uses a scientific modeling system to study the level of breast cancer incidence and mortality of the population in the USA between 1975 and 2000. Four interactive processes are modeled simultaneously: Natural History of Breast Cancer, Breast Cancer Detection, Breast Cancer Treatment, and Breast Cancer Mortality. These components form a complex interactive system that simulates the lives of 2,950,000 women (approximately 1/50 of the U.S. population) from 1950 to 2000 in 6-month cycles. After a learning period of 25 years, the outputs of the model provide the incidence and mortality rates as a function of age between 1975 and 2000 (Guller in Big data analytics with spark: a practitioner’s guide to using spark for large scale data analysis. Apress, Berzkeley, CA, USA, 2015). The model also simulates cases of disease, both detected and hidden, at the individual level, and can also be Used to answer questions about treatment selection and effectiveness, as well as to estimate the benefits for women of specific ages and histories.KeywordsBreast cancer detectionBreast cancer survivalData miningWekaLearning" @default.
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- W3138313893 date "2021-01-01" @default.
- W3138313893 modified "2023-10-18" @default.
- W3138313893 title "Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence" @default.
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- W3138313893 doi "https://doi.org/10.1007/978-981-33-4788-5_5" @default.
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