Research Article
A Comparative Evaluation of Kaplan-Meier, Cox Proportional Hazards, and Random Survival Forests for Neonatal Mortality Prediction
Issue:
Volume 13, Issue 2, December 2025
Pages:
42-59
Received:
2 September 2025
Accepted:
12 September 2025
Published:
27 October 2025
DOI:
10.11648/j.cbb.20251302.11
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Abstract: Neonatal mortality remains a critical public health challenge, particularly in low- and middle-income countries (LMICs), where limited healthcare resources and fragmented follow-up systems hinder timely interventions. Accurate prediction of neonatal death is essential for risk stratification, resource allocation, and improving survival outcomes. While traditional survival analysis methods like the Kaplan-Meier estimator and Cox proportional hazards (Cox PH) model are widely used, they face limitations in handling non-linear relationships, high-dimensional data, and violations of proportional hazards assumptions. Random Survival Forests (RSF), a machine learning approach, offers potential advantages but lacks sufficient comparative evaluation in neonatal mortality prediction, especially within LMIC contexts. This study aimed to comparatively evaluate the performance of Kaplan-Meier, Cox PH, and RSF models in predicting neonatal mortality using a synthetic dataset reflecting perinatal epidemiology in Kenya. The research addresses a significant and direct methodological comparisons across these models in neonatal populations, particularly under real-world conditions involving censoring, missing data, and non-proportional hazards. We assessed discrimination (C-index, time-dependent AUC), calibration (Integrated Brier Score, CRPS), and clinical interpretability. The dataset included 2,000 neonates with 17 covariates including but not limited to gestational age, birth weight, maternal health, and socioeconomic status. Results showed that RSF outperformed both Kaplan-Meier and Cox PH in discrimination (C-index: 0.875 vs. 0.868) and maintained strong calibration, particularly at 28 days. Variable importance measures identified gestational age, birth weight, and maternal health score as top predictors. SHAP values enhanced interpretability of RSF outputs. The Cox model provided clinically intuitive hazard ratios but was less flexible in capturing interactions. The study concluded that RSF offers superior predictive accuracy for neonatal mortality and should be integrated into risk prediction tools, especially in data-rich settings. Policy makers should support adoption of advanced analytics in perinatal care systems, while maintaining traditional models for inferential clarity. Combining both paradigms can optimize neonatal survival strategies.
Abstract: Neonatal mortality remains a critical public health challenge, particularly in low- and middle-income countries (LMICs), where limited healthcare resources and fragmented follow-up systems hinder timely interventions. Accurate prediction of neonatal death is essential for risk stratification, resource allocation, and improving survival outcomes. Wh...
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,
Denis Kariuki Muriithi
