From: Application of machine learning to identify risk factors of birth asphyxia
Algorithms | ROC_AUC | Accuracy |
---|---|---|
Logistic Regression | 0.88 | 0.88 |
Decision Tree Classification | 0.98 | 0.98 |
Random Forest Classification | 0.99 | 0.99 |
XGBoost Classification | 0.93 | 0.92 |
Permutation Feature Classification with KNN | 0.98 | 0.98 |
Light GBM | 0.93 | 0.93 |
Deep Learning-Feed Forward | 1.0 | 0.98 |
SVM | 0.88 | 0.88 |