Table 2 Performance metrics of SIS prediction models.

From: Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke

Model

Accuracy (%)

Precision

Recall

F1 scores

AUC-ROC

SIS main scale

RF

85

0.88

0.85

0.85

0.86

KNN

75

0.76

0.75

0.75

0.8

ANN

75

0.77

0.75

0.74

0.87

SVM

72

0.73

0.73

0.73

0.71

LG

72.82

0.73

0.73

0.72

0.77

SIS global rating scale

RF

80

0.78

0.8

0.78

0.75

KNN

82.5

0.82

0.83

0.81

0.76

ANN

77.5

0.77

0.78

0.77

0.75

SVM

77.5

0.77

0.78

0.77

0.68

LG

77.5

0.78

0.78

0.78

0.75

  1. SIS Stroke Impact Scale, RF random forest, KNN k-nearest neighbors, ANN artificial neural network, SVM support vector machine, LG logistic regression, AUC-ROC area under the receiver operating characteristic curve.