Child Mortality Prediction in Bangladesh: A Decade-Long Validation Study
This provides a robust, production-ready tool for targeted maternal and child health interventions in Bangladesh, though it is incremental as it improves on existing methods with specific validation and fairness insights.
This study tackled the problem of inaccurate child mortality prediction models due to look-ahead bias by training on 2011-2014 data, validating on 2017 data, and testing on 2022 data from Bangladesh, finding that a neural architecture search model outperformed XGBoost with an AUROC of 0.76 vs. 0.73 and could identify approximately 1300 additional at-risk children annually.
The predictive machine learning models for child mortality tend to be inaccurate when applied to future populations, since they suffer from look-ahead bias due to the randomization used in cross-validation. The Demographic and Health Surveys (DHS) data from Bangladesh for 2011-2022, with n = 33,962, are used in this paper. We trained the model on (2011-2014) data, validated it on 2017 data, and tested it on 2022 data. Eight years after the initial test of the model, a genetic algorithm-based Neural Architecture Search found a single-layer neural architecture (with 64 units) to be superior to XGBoost (AUROC = 0.76 vs. 0.73; p < 0.01). Additionally, through a detailed fairness audit, we identified an overall "Socioeconomic Predictive Gradient," with a positive correlation between regional poverty level (r = -0.62) and the algorithm's AUC. In addition, we found that the model performed at its highest levels in the least affluent divisions (AUC 0.74) and decreased dramatically in the wealthiest divisions (AUC 0.66). These findings suggest that the model is identifying areas with the greatest need for intervention. Our model would identify approximately 1300 additional at-risk children annually than a Gradient Boosting model when screened at the 10% level and validated using SHAP values and Platt Calibration, and therefore provide a robust, production-ready computational phenotype for targeted maternal and child health interventions.