Bibliographic Details
| Title: |
Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques |
| Authors: |
Azadeh Alizargar, Yang-Lang Chang, Tan-Hsu Tan |
| Source: |
Bioengineering, Vol 10, Iss 4, p 481 (2023) |
| Publisher Information: |
MDPI AG, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Technology
LCC:Biology (General) |
| Subject Terms: |
machine learning techniques, hepatitis C virus, data mining, decision tree, HCV, performance measurements, Technology, Biology (General), QH301-705.5 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2306-5354 |
| Relation: |
https://www.mdpi.com/2306-5354/10/4/481; https://doaj.org/toc/2306-5354 |
| DOI: |
10.3390/bioengineering10040481 |
| Access URL: |
https://doaj.org/article/035b8893eb3e45c5aa91a8c1d0ec345a |
| Accession Number: |
edsdoj.035b8893eb3e45c5aa91a8c1d0ec345a |
| Database: |
Directory of Open Access Journals |
