Child delivery mode prediction: exploring machine learning algorithms and dataset organizations

Abstract Accurate classification of child delivery mode is crucial for improving maternal and neonatal health. In developing countries like Ethiopia, clinical assessments alone often result in misguided medical interventions. Even though machine learning in healthcare has brought promises, the various algorithms along with different real-world datasets perform differently. Hence, the objective of this study was to develop a machine learning model for predicting child delivery mode based on real data. The study followed experimental and exploratory research design utilizing 1,072 antenatal records from Arba Minch General Hospital and Birbir Health Center, Ethiopia. 16 attributes were considered including the outcome, mode of delivery. Predictors included sociodemographic and clinical variables such as age, weight, blood pressure, previous CS, and fetal presentation. Five machine learning algorithms including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and CatBoost were trained and evaluated using hold-out validation. Additionally, a recent deep learning model, TabPFN, and Long Short-Term Memory were examined to expand the exploration. The results showed that RF (93.1 %) achieved the highest accuracy. TabPFN scored the second best accuracy score (92.5 %), demonstrating its potential on smaller tabular data. LSTM performed better than SVM and LR highlighting the consideration of the inherent temporal characteristics of the data.