Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
Azadeh Alizargar1, Yang-Lang Chang1, Tan-Hsu Tan1
1Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan.
Insights
Machine learning models can predict Hepatitis C virus (HCV) infection using routine blood tests. Support Vector Machine (SVM) and XGBoost show high accuracy, aiding early diagnosis and preventing liver damage.
Area of Science:
- Hepatology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Hepatitis C virus (HCV) infection is a significant cause of liver disease.
- Late symptom onset complicates early diagnosis and treatment of Hepatitis C.
- Early detection is crucial to prevent irreversible liver damage.
Purpose of the Study:
- To develop and evaluate machine learning models for early Hepatitis C prediction.
- To utilize common and affordable blood test data for disease prediction.
- To identify the most effective machine learning algorithm for Hepatitis C diagnosis.
Main Methods:
- Six machine learning algorithms were employed: Support Vector Machine (SVM), K-nearest Neighbors (KNN), Logistic Regression, Decision Tree, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN).
- Two datasets (NHANES and UCI) were used for model training and validation.
- Model performance was assessed using metrics including accuracy, AUC, precision, recall, F1 score, and ROC analysis.
Main Results:
- Support Vector Machine (SVM) and XGBoost demonstrated the highest predictive performance.
- Both SVM and XGBoost achieved accuracy and Area Under the Curve (AUC) values exceeding 80%.
- These models proved effective in predicting Hepatitis C using standard blood test results.
Conclusions:
- Machine learning, particularly SVM and XGBoost, offers a viable approach for early Hepatitis C prediction.
- Routine blood tests combined with advanced algorithms can support medical professionals in timely diagnosis.
- This predictive capability can lead to earlier treatment and improved patient outcomes.
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 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.
