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

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 techniq...

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Detalles Bibliográficos
Autores principales: Alizargar, Azadeh, Chang, Yang-Lang, Tan, Tan-Hsu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10135598/
https://www.ncbi.nlm.nih.gov/pubmed/37106668
http://dx.doi.org/10.3390/bioengineering10040481
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author Alizargar, Azadeh
Chang, Yang-Lang
Tan, Tan-Hsu
author_facet Alizargar, Azadeh
Chang, Yang-Lang
Tan, Tan-Hsu
author_sort Alizargar, Azadeh
collection PubMed
description 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 operating characteristics (ROC), and the area under the curve (AUC) to identify a method that is appropriate for predicting this disease. The analysis, on NHANES and UCI datasets, revealed that SVM and XGBoost (with the highest accuracy and AUC among the test models, >80%) can be effective tools for medical professionals using routine and affordable blood test data to predict hepatitis C.
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spelling pubmed-101355982023-04-28 Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Bioengineering (Basel) Article 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 operating characteristics (ROC), and the area under the curve (AUC) to identify a method that is appropriate for predicting this disease. The analysis, on NHANES and UCI datasets, revealed that SVM and XGBoost (with the highest accuracy and AUC among the test models, >80%) can be effective tools for medical professionals using routine and affordable blood test data to predict hepatitis C. MDPI 2023-04-17 /pmc/articles/PMC10135598/ /pubmed/37106668 http://dx.doi.org/10.3390/bioengineering10040481 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alizargar, Azadeh
Chang, Yang-Lang
Tan, Tan-Hsu
Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
title Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
title_full Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
title_fullStr Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
title_full_unstemmed Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
title_short Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
title_sort performance comparison of machine learning approaches on hepatitis c prediction employing data mining techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10135598/
https://www.ncbi.nlm.nih.gov/pubmed/37106668
http://dx.doi.org/10.3390/bioengineering10040481
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