Liver Cancer Classification Using Random Forest and Extreme Gradient Boosting (XGBoost) with Genetic Algorithm as Feature Selection

Vabiyana Safira Desdhanty, Zuherman Rustam

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

2 Citations (Scopus)

Abstract

Cancer has become a common cause of death in the world with approximately ten million deaths every year. Liver cancer is one of the common cancers that can happen to both men and women. Therefore, early detection is very important to help patients treating the cancer before it spreads to another organ. Machine Learning has been a huge help in many fields including the medical fields for classifying cancer data so the diagnosis can be more accurate. For that, feature selection is needed to increase the accuracy even more. This paper will be focusing on the implementation of Genetic Algorithm as feature selection when applied to the widely used machine learning algorithm Random Forest and Extreme Gradient Boosting (XGBoost) for cancer classification. The result will show that with 20% testing data, XGBoost with Genetic Algorithm as feature selection gives the highest accuracy score at 82%.

Original languageEnglish
Title of host publication2021 International Conference on Decision Aid Sciences and Application, DASA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages716-719
Number of pages4
ISBN (Electronic)9781665416344
DOIs
Publication statusPublished - 2021
Event2021 International Conference on Decision Aid Sciences and Application, DASA 2021 - Virtual, Online, Bahrain
Duration: 7 Dec 20218 Dec 2021

Publication series

Name2021 International Conference on Decision Aid Sciences and Application, DASA 2021

Conference

Conference2021 International Conference on Decision Aid Sciences and Application, DASA 2021
Country/TerritoryBahrain
CityVirtual, Online
Period7/12/218/12/21

Keywords

  • Classification
  • Genetic Algorithm
  • Liver Cancer
  • Random Forest
  • XGBoost

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