Hepatitis classification using support vector machines and random forest

Abstract

<span id="docs-internal-guid-e57881bf-7fff-62db-2c1e-192664c8e8a8"><span>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.</span></span>

Dengue classification method using support vector machines and cross-validation techniques

“…There are five classification methods implemented in this study, consisting of C.45, DT, KNN, RF, and SVM. Those classification method has been successfully implemented in several previous studies [6], [8], [16]. Classification is done using a cross-validation technique with k-fold 3, 5, and 10 to distribute training and testing data [6].…”

“…RF is a file-producing machine learning method that is flexible and simple to use. Even without hyperparameter tweaking, a superb outcome will produce most of the time [8]. The RF is one of the most extensively used algorithms due to its simplicity and diversity.…”

“…Expert systems can provide a more accessible and helpful way for human experts to develop and test new theories, especially in healthcare. The data used in the expert system can vary, such as images [2]- [4], signals [5], or medical record data which includes name, age, laboratory test results, and symptoms of the patient [6]- [8]. In the medical field, several expert systems have been developed, for example for estimating drug doses [6], [9], monitoring the disease progress [1], [2], [10], and detecting several types of diseases such as diabetes mellitus [11], pancreatic cancer [12], breast cancer [13], glaucoma [14], and dengue fever (DF) [15]- [17].…”

“…It can be applied using several methods, such as rule-based [21]- [24], or using machine learning [25]. The following methods were used in prior studies to implement the machine learning-based classification process: naive Bayes [6], logistic regression [12], random forest (RF) [8], [12], [16], k-nearest neighbor (KNN) [26], artificial neural network (ANN) [10], [13], dan support vector machine (SVM) [8], [14].…”

“…The proposed SVM and RF methods succeeded in predicting the data correctly. They managed to achieve the best results with a value of 0.995 [8]. Comparison of SVM kernel selection implemented on diabetes dataset using linear SVM, polynomial SVM, and RBF kernel.…”