Comparing the performance of statistical and machine learning survival models in predicting timing and determinants of postnatal care in Rwanda

作者：Kubahoniyesu T, Kayijuka I, Niragire F · 发表于：BMC pregnancy and childbirth · 年份：2026 · DOI：10.1186/s12884-026-09313-9

BACKGROUND: Postnatal care (PNC) remains the least utilized component of the maternal care continuum despite its critical role in preventing maternal and neonatal deaths. Conventional survival models estimate associations and identify determinants of time to postnatal care, whereas machine-learning survival models aim to improve prediction by capturing complex nonlinear relationships in the data. METHODS: This was a cross-sectional study based on a secondary analysis of the 2019-2020 Rwanda Demographic and Health Survey (RDHS) data for 3,135 women who had a live birth within two years preceding the survey. Data pre-processing addressed missingness through model-based and group-wise imputation. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO-L1), which identified ten predictors. For conventional modelling, time to first PNC was analyzed using Cox proportional hazards and accelerated failure time (AFT) models to estimate associations with PNC timing. For machine-learning (ML) modelling, Random Survival Forests (RSF), Gradient Boosted Survival Trees (GBST), and Deep Survival Analysis (DeepSurv) were applied to predict time to PNC by capturing nonlinear relationships among predictors. Model performance was evaluated using the concordance index (C-index) and integrated Brier score (IBS), while model fit for likelihood-based models was assessed using Akaike (AIC) and Bayesian (BIC) information criteria. RESULTS: The median time to ...