Fig 1.
Study workflow.
Table 1.
Patient characteristics.
Fig 2.
HCV genomic variants (Gene region).
Fig 3.
HCV genomic variants (SVR vs. non-SVR).
Fig 4.
Generalization performance of machine-learning algorithms.
SVM: Support vector machine, NN: Neural network, RF: Random forest, LR: Logistic regression, GBM: Gradient boost machine, KNN: K-nearest neighbor, FDA: Flexible discriminant analysis, DT: Decision tree, NB: naive Bayesian.
Fig 5.
Training profile of machine-learning algorithms.
PPV: Positive predictive value, NPV: Negative predictive value, SVM: Support vector machine, NN: Neural network, KNN: K-nearest neighbor, LR: Logistic regression, RF: Random forest, FDA: Flexible discriminant analysis, GBM: Gradient boost machine, DT: Decision tree, NB: Naive Bayesian.
Fig 6.
Correlation matrices of important variables.
SVM: Support vector machine, RF: Random forest, GBM: Gradient boost machine, NB: Naive Bayesian, KNN: K-nearest neighbor, LR: Logistic regression, NN: Neural network, FDA: Flexible discriminant analysis, DT: Decision tree.
Table 2.
Performance evaluation of machine learning algorithms.