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 |