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Table 1 AI models for drug-included liver injury

From: Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination

Dataset ML method AUC CA F1 Precision Recall
Training set SGD 0.647 0.709 0.715 0.723 0.709
kNN 0.654 0.722 0.698 0.690 0.722
SVM 0.785 0.795 0.791 0.788 0.795
DT 0.710 0.756 0.760 0.764 0.756
RF 0.814 0.838 0.827 0.832 0.838
Adaboost 0.785 0.792 0.788 0.785 0.792
ANN 0.621 0.737 0.671 0.685 0.737
LR 0.746 0.776 0.757 0.757 0.761
NB 0.632 0.686 0.694 0.711 0.686
Test set SGD 0.627 0.682 0.694 0.712 0.682
kNN 0.574 0.745 0.636 0.555 0.745
SVM 0.669 0.747 0.712 0.710 0.747
DT 0.544 0.680 0.679 0.678 0.680
RF 0.739 0.767 0.731 0.739 0.767
Adaboost 0.614 0.708 0.707 0.707 0.708
ANN 0.647 0.694 0.696 0.697 0.694
LR 0.656 0.733 0.694 0.688 0.733
NB 0.598 0.675 0.648 0.705 0.675
  1. LR Logistic regression, RF Random forest, SVM Support vector machine, kNN k-nearest neighbor, DT Decision tree, NB Naive bayes, ANN Artificial neural network, SGD Stochastic gradient descent