Comparison between Support Vector Machine and Random Forest for Hepatocellular Carcinoma (HCC) Classification

Velery Virgina Putri Wibowo, Zuherman Rustam, Sri Hartini, Qisthina Syifa Setiawan, Jane Eva Aurelia

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

Abstract

Hepatocellular Carcinoma (HCC) is a type of liver cancer which occurs when a tumor grows malignantly in the liver. This cancer starts from the liver and is not caused by the spread of cancer from other organs. HCC commonly occurs due to the complications of liver disease. However, most patients do not show signs and symptoms in the early stage of liver cancer. Therefore, classification with high accuracy is needed to predict individuals with HCC early, based on their data and to provide them with the best treatment. In this study, the data used consisted of 192 samples with 66 HCC and 126 non-HCC samples, which were obtained from Al Islam Bandung Hospital. Many machine learning methods have been used to carry out classification. Among these methods, Support Vector Machine (SVM) and Random Forest (RF) have been frequently used due to their high level of performance. Therefore, in this study, SVM and RF were compared and analyzed for the classification of HCC. The aim of this study was to discover which method has the best accuracy to classify HCC. The results showed that SVM and RF had the highest accuracy value at 90% and 100% respectively. Therefore, RF method is a better model compared to SVM and suggested to be used in the classification of HCC.

Original languageEnglish
Title of host publication2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages618-622
Number of pages5
ISBN (Electronic)9781728196770
DOIs
Publication statusPublished - 8 Nov 2020
Event2020 International Conference on Decision Aid Sciences and Application, DASA 2020 - Virtual, Sakheer, Bahrain
Duration: 7 Nov 20209 Nov 2020

Publication series

Name2020 International Conference on Decision Aid Sciences and Application, DASA 2020

Conference

Conference2020 International Conference on Decision Aid Sciences and Application, DASA 2020
CountryBahrain
CityVirtual, Sakheer
Period7/11/209/11/20

Keywords

  • Classification
  • Hepatocellular Carcinoma
  • Machine Learning
  • Random Forest
  • Support Vector Machine

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