DCA revealed potential benefits of applying Boosting method for predicting low Apgar scores among the tested models. A slight improvement was observed in the F1 score, BA, and BM. SMOTE, ROS and and RUS techniques were more effective at improving “recalls” among other metrics in all the models under investigation. Resultsīirth weight, maternal age, and gestational age were found to be important predictors for the low Apgar score following induced vaginal delivery. We applied Decision Curve Analysis (DCA) to evaluate the net benefit of the selected classifiers. To address class imbalances, we examined three widely used resampling techniques: the Synthetic Minority Oversampling Technique (SMOTE) and Random Oversampling Examples (ROS) and Random undersampling techniques (RUS). We used Area Under Curve (AUC), recall, precision, F-score, Matthews Correlation Coefficient (MCC), balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK) to evaluate the performance of the selected six (6) machine learning classifiers. The ‘extra-tree classifier’ was used to assess features’ importance. 733 (9.5%) of which constituted of low (< 7) Apgar score neonates. We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective. Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes.
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