Scholarlink

Background <p>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.</p> Methods <p>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.</p> Results <p>The median time to first PNC was 24&#xa0;h. Within 24&#xa0;h, 54.9% (95% CI: 53.2–56.7) of women received first PNC; 70.4% (95% CI: 69.8–73.0) received PNC within 48&#xa0;h, while 25.8% did not receive PNC by 42 days (censored at 1,008&#xa0;h). Higher hazards (earlier PNC uptake) were observed for cesarean compared to vaginal delivery (AHR = 1.80; 95% CI: 1.58–2.05; <i>p</i> &lt; 0.001), health center compared to home delivery (AHR = 1.41; 95% CI: 1.19–1.68; <i>p</i> &lt; 0.001), at least four ANC visits compared to women with no ANC (AHR = 1.64; 95% CI: 1.05–2.57; <i>p</i> = 0.030), insured compared to uninsured women (AHR = 1.13; 95% CI: 1.00–1.28; <i>p</i> = 0.045), and regular media exposure (AHR = 1.30; 95% CI: 1.16–1.45; <i>p</i> &lt; 0.001). Machine learning models showed better predictive performance than classical models. The GBST performed best overall (C-index = 0.78; IBS = 0.142), followed by RSF (C-index = 0.76; IBS = 0.153). Among classical models, the log–logistic AFT model provided the best fit (C-index = 0.72; IBS = 0.168).</p> Conclusion <p>Machine learning survival models showed improved predictive performance for predicting time to first PNC compared with classical survival models. These findings suggest that ML may complement conventional approaches for risk stratification and prediction of postnatal care timing in Rwanda.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

查看全文
  • Theogene Kubahoniyesu,
  • Idrissa Kayijuka,
  • François Niragire

摘要

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 first PNC was 24 h. Within 24 h, 54.9% (95% CI: 53.2–56.7) of women received first PNC; 70.4% (95% CI: 69.8–73.0) received PNC within 48 h, while 25.8% did not receive PNC by 42 days (censored at 1,008 h). Higher hazards (earlier PNC uptake) were observed for cesarean compared to vaginal delivery (AHR = 1.80; 95% CI: 1.58–2.05; p < 0.001), health center compared to home delivery (AHR = 1.41; 95% CI: 1.19–1.68; p < 0.001), at least four ANC visits compared to women with no ANC (AHR = 1.64; 95% CI: 1.05–2.57; p = 0.030), insured compared to uninsured women (AHR = 1.13; 95% CI: 1.00–1.28; p = 0.045), and regular media exposure (AHR = 1.30; 95% CI: 1.16–1.45; p < 0.001). Machine learning models showed better predictive performance than classical models. The GBST performed best overall (C-index = 0.78; IBS = 0.142), followed by RSF (C-index = 0.76; IBS = 0.153). Among classical models, the log–logistic AFT model provided the best fit (C-index = 0.72; IBS = 0.168).

Conclusion

Machine learning survival models showed improved predictive performance for predicting time to first PNC compared with classical survival models. These findings suggest that ML may complement conventional approaches for risk stratification and prediction of postnatal care timing in Rwanda.