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 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.

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models

“…Yağanoğlu et al [ 27 ] used feature extraction techniques to obtain new features and trained the dataset on multiple classifiers, DT performed the best, obtaining 99.31% accuracy and 0.98 AUC. According to the most recent study to our knowledge, Alizargar et al [ 28 ] proposed a method using XGboost to get 0.984 AUC and 95% accuracy. Compared with the above-proposed method, our proposed IHCP model obtained 99.44% accuracy and 0.9986 AUC, both of which were improved compared with the previous method.…”

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions

“…Fortunately, the technology at our disposal offers cost-effective solutions to these problems. Machine learning (ML) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], artificial intelligence (AI) [16][17][18][19][20][21][22][23][24][25][26][27][28][29] and Big Data [30][31][32][33][34][35] can provide robust and innovative answers to long-standing problems.…”