Development and validation of a machine learning model for prediction of cephalic dystocia.

This suggests that while certain variables did not reach statistical significance in the LR model, they may still play a crucial role in capturing complex nonlinear relationships in tree-based models, thereby enhancing overall predictive capability.

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