Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. 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. METHODS: We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). 733 (9.5%) of which constituted of low (< 7) Apgar score neonates. The 'extra-tree classifier' was used to assess features' importance. 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. 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 applied Decision Curve Analysis (DCA) to evaluate the net benefit of the selected classifiers. RESULTS: Birth weight, maternal age, and gestational age were found to be important predictors for the low Apgar score following induced vaginal delivery. SMOTE, ROS and and RUS techniques were more effective at improving "recalls" among other metrics in all the models under investigation. A slight improvement was observed in the F1 score, BA, and BM. DCA revealed potential benefits of applying Boosting method for predicting low Apgar scores among the tested models. CONCLUSION: There is an opportunity for more algorithms to be tested to come up with theoretical guidance on more effective rebalancing techniques suitable for this particular imbalanced ratio. Future research should prioritize a debate on which performance indicators to look up to when dealing with imbalanced or skewed data.

Knowledge, attitudes, and behaviors with mask use and vaccines for COVID-19 prevention at 13 colleges and universities, April 2021.

Objectives: To understand college and university student knowledge, attitudes, and behaviors (KAB) regarding COVID-19 prevention strategies. Methods: Thirteen colleges and universities volunteered to conduct an anonymous electronic survey in April 2021 to assess students' KAB about mask use and vaccination to prevent COVID-19. Results: Three-quarters of students indicated they "Always" wore a mask correctly when in public indoor places. Of those not yet vaccinated, 55% expressed concern about unknown side effects. Over half of students were unsure or believe they do not need to continue wearing masks after vaccination and older students more likely to be vaccinated. There was a significant inverse correlation between intention of getting vaccinated and intention to attend a large indoor party without a mask. Conclusions: Colleges and universities are important to community efforts to slow the COVID-19 pandemic. The KAB findings can inform approaches to increase overall mask use and vaccination uptake among young students.