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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10135598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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