Ovarian cancer data classification using bagging and random forest

A. Arfiani, Z. Rustam

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

Abstract

Ovarian cancer is the fifth most common cause of cancer deaths in women worldwide. Most cases of ovarian cancer occur in women entering menopause or in age of 50 years onwards. One step to reducing mortality from ovarian cancer is timely detection and effective treatment. Accurate and efficient method is needed for the gaining insight on ovarian cancer, particularly in classification of benign or malignant, as the focus of this paper. There have been many ways used to classify ovarian cancer including machine learning methods. In this paper, we proposed the machine learning method, namely Bagging and Random Forest for the classification into the benign or malignant of ovarian cancer. Bagging method is known to maximize classification and prevent overfitting. Whereas, the Random Forest can produce low errors, and is an effective method for estimating missing data. We use microarray data obtained from UCI Machine Learning Repository downloaded on September 2018. Simulations on training data were carried out with various percentage. In each simulation, accuracy and running time were calculated. The final score of experimental result confirmed that bagging reached 100 % accuracy for 90 % training data, while the Random Forest achieved an accuracy of 98.2 % for 90 % training data.

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

  • Bootstrap aggregating
  • microarray datasets
  • random forest

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  • Cite this

    Arfiani, A., & Rustam, Z. (2019). Ovarian cancer data classification using bagging and random forest. In T. Mart, D. Triyono, & I. T. Anggraningrum (Eds.), Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018 [020046] (AIP Conference Proceedings; Vol. 2168). American Institute of Physics Inc.. https://doi.org/10.1063/1.5132473