Table 4 Results of the predictive modeling for the clinical features. XGBoost: eXtreme Gradient Boosting; LSVM: Lagrangian Support Vector Machine; Quest: Random Trees, and Quick, Unbiased, Efficient Statistical Tree; MLP-NN: multiplayer layer perceptron neural network; RBF-NN: radial basis function neural network
From: Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery
Algorithm | Accuracy | |
|---|---|---|
Training | ||
Random Trees | 95.46 | |
XGBoost Tree | 100 | |
LSVM | 89.58 | |
SVM | 88.00 | |
CHAID | 89.79 | |
MLP-NN | 90.4 | |
RBF-NN | 87.4 | |
Testing | ||
Random Trees | 89.74 | |
XGBoost Tree | 90.49 | |
LSVM | 83.84 | |
SVM | 90.17 | |
CHAID | 85.46 | |
MLP-NN | 82.6 | |
RBF-NN | 91.5 |