Hepatitis classification using support vector machines and random forest

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


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 are applied to predict hepatitis data, and then the results will be compared.


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

DOI: http://doi.org/10.11591/ijai.v10.i2.pp%25p


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