Worldwide, the prevalence of the hepatitis C virus (HCV) is quite high, and the disease's progression can result in irreparable liver damage or even death. Because of this, machine learning techniques can be used to construct prediction models. Different classification algorithms were used in this study to classify HCV-infected patients. An analysis of the UCI Machine Learning Repository's dataset was carried out for this work. The synthetic minority oversampling technique (SMOTE) was used since the HCV dataset was uneven. To create six classification models, the dataset was separated into training and test data. The support vector machine (SVM), Gaussian Nave Bayes (NB), decision tree (DT), random forest (RF), logistic regression (LR), and K-nearest neighbours (KNN) method were all included in this set of six algorithms. The classifiers were developed in Python, a popular computer language. The performance of the proposed models was evaluated using measures such as the receiver operating characteristic curve (ROC) and others.

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