Random forest for breast cancer prediction

T. L. Octaviani, Z. Rustam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

One of cancer that commonly causes most of the death is breast cancer. According to WHO data published in 2017, breast cancer deaths in Indonesia reached 21,287 or 1.27 % of total deaths. Delay in knowing of the condition of breast cancer in women with breast cancer, results in increased mortality, poor prognosis, and decreased survival rates, which are also associated with lower awareness of breast cancer, and also recommended non-adherence to screening. In this paper, we propose a random forest for breast cancer prediction. Random forest is one of many classification techniques, and it is an algorithm for big data classification. Random forest classification is applied to cancer microarray data to achieve a more accurate and reliable classification performance. The accuracy in this paper is 100 %.

Original languageEnglish
Title of host publicationProceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018
EditorsTerry Mart, Djoko Triyono, Ivandini T. Anggraningrum
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419155
DOIs
Publication statusPublished - 4 Nov 2019
Event4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018 - Depok, Indonesia
Duration: 30 Oct 201831 Oct 2018

Publication series

NameAIP Conference Proceedings
Volume2168
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018
CountryIndonesia
CityDepok
Period30/10/1831/10/18

Keywords

  • Breast cancer
  • machine learning
  • prediction
  • random forest

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  • Cite this

    Octaviani, T. L., & Rustam, Z. (2019). Random forest for breast cancer prediction. In T. Mart, D. Triyono, & I. T. Anggraningrum (Eds.), Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018 [020050] (AIP Conference Proceedings; Vol. 2168). American Institute of Physics Inc.. https://doi.org/10.1063/1.5132477