Classification of cancer data based on support vectors machines with feature selection using genetic algorithm and Laplacian score

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Cancer is one of the most deadly diseases for humans. According to the WHO (2015), cancer is the causes of the death number two in the world by 13 % after cardiovascular disease. Cancer often causes death if treatment is too late. Therefore, early detection of cancer is necessary to avoid the spread of cancer. High-dimensional medical data is one of the obstacles to the application of machine learning techniques due to a negative effect on the process of analysis. Therefore, the selection features required to increase performance in the detection of cancer. This paper focuses on the comparison of feature selection on cancer data. We use Genetic Algorithm and Laplacian Score for cancer gene selection of features, coupled with the Support Vectors Machines for cancer classification. The results will show that Genetic Algorithm gives the best accuracy with the percentage of 98.69 % only using 40 features.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • cancer
  • classification
  • genetic algorithm
  • laplacian score
  • support vectors machines

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