Hepatitis classification using support vector machines and random forest

Jane Eva Aurelia, Zuherman Rustam, Ilsya Wirasati, Sri Hartini, Glori Stephani Saragih

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute hepatitis patients. This research uses data of hepatitis examination result with amount of 113 data and 5 features. The method that used is support vector machines (SVM) and random forest method. SVM is the classification method that uses discriminant hyper-plane, dividing to classes. meanwhile, random forest is a tree-based ensemble depending on a collection of random variables. SVM and random forest (RF) are applied to predict hepatitis data, and then the results will be compared.

Original languageEnglish
Pages (from-to)446-451
Number of pages6
JournalIAES International Journal of Artificial Intelligence
Volume10
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • Classification
  • Hepatitis
  • Machine learning
  • Random forest
  • Support vector machines

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