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Author Abreu, Pedro Henriques ♦ Santos, Miriam Seoane ♦ Abreu, Miguel Henriques ♦ Andrade, Bruno ♦ Silva, Daniel Castro
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Breast cancer recurrence ♦ Clinical decision-making ♦ Pattern recognition
Abstract Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.
Description Author Affiliation: LIACC, Department of Informatics Engineering, Faculty of Engineering of Porto University, Portugal (Silva, Daniel Castro); CISUC, Department of Informatics Engineering, Faculty of Sciences and Technology of Coimbra University, Portugal (Abreu, Pedro Henriques; Santos, Miriam Seoane; Andrade, Bruno); Portuguese Institute of Oncology of Porto, Portugal (Abreu, Miguel Henriques)
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-10-12
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 3
Page Count 40
Starting Page 1
Ending Page 40


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Source: ACM Digital Library