<![CDATA[Hepatitis C (HCV) is an RNA virus and one of the blood-borne human pathogens known as Hepatitis C. According to the World Health Organization (WHO), it is estimated that nearly 3% or 120-130 million of the world's population are infected with HCV and 3-4 million new infection cases. Early diagnosis of HCV has not been effective so most of the factors that contribute to the disease are still unclear. This study aims to implement a machine learning algorithm to identify factors that contribute to hepatitis C virus and hepatitis C virus prediction problems by comparing each algorithm to determine the best algorithm for predicting hepatitis C virus in the HCV UCI Machine Learning Repository dataset. Six classification algorithms are proposed: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Random Forest. The results show that from the accuracy value of each algorithm, the best algorithm for predicting hepatitis C virus is random forest with an accuracy rate of 98.37% and it was found that the features that contributed the most to the prediction model for HCV-infected and non-HCV patients were AST (Aspartate aminotransferase) and ALP (alkaline phosphatase).]]>
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