Survival Machine-Learning Approach for Predicting Under-Five Mortality in Low Sociodemographic Index States of India.

Abstract

Background: Each year, millions of children under five die globally, with many of these deaths being preventable. The situation is particularly concerning in low sociodemographic index (LSDI) states of India, where the under-five mortality rate is 45 children per 1000 live births. This study aimed to predict under-five mortality and determine related key factors. Study Design: A cross-sectional study.

Methods: This study analyzed National Family Health Survey-5 (NFHS-5) data related to 94,202 children from the LSDI states of India. Several survival models were tested, including Cox proportional hazards, random survival forest, and gradient-boosted survival, to identify factors linked to child mortality. Model performance was evaluated using metrics such as the concordance index, integrated Brier score, and time-dependent receiver operating characteristic (ROC) curves.

Results: Among the studied children, 4.5% (4,284) died before their fifth birthday. The risk of death was higher in children born to younger (15-25 years) mothers (hazard ratio [HR] = 1.113, 95% confidence interval (CI): 1.034, 1.198; P < 0.001), uneducated mothers (HR = 1.263, 95% CI: 1.098-1.454; P < 0.0001), mothers with a poorer wealth index (HR = 1.719, 95% CI: 1.475-2.003; P < 0.0001), and children with low birth weight (HR = 2.091, 95% CI: 1.934-2.26; P < 0.001). The random survival forest model outperformed in identifying these risk factors.

Conclusion: This study highlights the importance of empowering women through education, improving family planning, addressing poverty, and providing equitable healthcare to reduce child mortality. These insights can help shape policies and initiatives to improve the survival and health of children in vulnerable communities.