Table 10 Accuracy of the classification by different classifiers for feature set S1 and S2.

From: A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set

Classifier

Statistical parameters of all the classifiers for feature set S1

Accuracy

Kappa

RMSE

Avg. TP

Avg. FP

Avg. Prec.

Avg. Recall

Avg. F-Score

Random Forest

96.91

0.952

0.974

0.969

0.016

0.969

0.969

0.969

MLP

97.94

0.968

0.81

0.979

0.013

0.979

0.979

0.979

Naïve Bayes

88.66

0.824

0.240

0.887

0.063

0.894

0.887

0.888

Simple Logistics

94.85

0.92

0.177

0.948

0.029

0.949

0.948

0.948

Classifier

Statistical parameters of all the classifiers for feature set S2

Accuracy

kappa

RMSE

Avg. TP

Avg. FP

Avg. Prec.

Avg. Recall

Avg. F-Score

Random Forest

63.92

0.317

0.398

0.639

0.329

0.602

0.639

0.618

MLP

57.73

0.236

0.490

0.577

0.332

0.566

0.577

0.571

Naïve Bayes

52.58

0.162

0.169

0.526

0.352

0.554

0.526

0.533

Simple Logistics

55.67

0.218

0.544

0.557

0.324

0.569

0.557

0.563