Diagnosis of hepatitis disease using machine learning techniques

Ibraheem I. Ahmed, Duraid Y. Mohammed, Khamis A. Zidan


Hepatitis is an infection that causes inflammation of liver tissue. Many studies have developed machine learning models for hepatitis disease diagnosis. However, there has been little discussion about the relationship between hepatitis symptoms. The first objective of this study is to provide a brief description of a real-world hepatitis disease symptom dataset. Furthermore, the authors proposed a stand-alone classification platform using random forest, decision tree, and support vector machine into healthy people or hepatitis patients using adaptive wrapper feature selection. It was discovered that there is a strong link between certain characteristics and hepatitis diagnosis. The work presented here may help improve hepatitis diagnosis in the early stages, which may lead to a reduction in the acute effects of hepatitis on human life. It is worth noting that random forest (RF) gave the highest accuracy and stayed slightly consistent through all sets of features in comparison to decision tree (DT) and support vector machines (SVM).


Adaptive feature selection; Automated hepatitis diagnosis; Decision tree; Hepatitis symptoms; Random forest; Support vector machine;

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DOI: http://doi.org/10.11591/ijeecs.v26.i3.pp1564-1572


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