Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques.

ABSTRACT: Hepatitis C is a liver infection caused by the hepatitis C virus (HCV). Due to the late onset of symptoms, early diagnosis is difficult in this disease. Efficient prediction can save patients before permeant liver damage. The main objective of this study is to employ various machine learning techniques to predict this disease based on common and affordable blood test data to diagnose and treat patients in the early stages. In this study, six machine learning algorithms (Support Vector Machine (SVM), K-nearest Neighbors (KNN), Logistic Regression, decision tree, extreme gradient boosting (XGBoost), artificial neural networks (ANN)) were utilized on two datasets. The performances of these techniques were compared in terms of confusion matrix, precision, recall, F1 score, accuracy, receiver o

SUBMITTER: Alizargar A 

PROVIDER: S-EPMC10135598 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature